Machine Learning Fungi ID.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n",
    "from sklearn.metrics import classification_report, confusion_matrix, f1_score, matthews_corrcoef\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn import linear_model, neighbors\n",
    "import pickle, time, glob, os, sys\n",
    "from skimage.color import rgb2gray\n",
    "import warnings\n",
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make feature vectors that have been transformed with fft\n",
    "def read_photo_data(metadata_file=\"Photos_data_by_group.csv\", data_col=\"Group\", example_photo=\"./Fungi Pics/JL1.tif\", sample=None):\n",
    "    '''\n",
    "    Function to read the photo metadata and extract important variables.\n",
    "    \n",
    "    Inputs:\n",
    "    ------\n",
    "    metadata_file : (str) Filepath to csv file that corresponds the photo to its classification.\n",
    "    \n",
    "    data_col : (str) Column of the file with the classifications.\n",
    "    \n",
    "    example_photo : (str) Filepath to an example photo for extracting dimensions.\n",
    "    \n",
    "    sample: (int, optional) Number of files to sample to reduce memory requirement.\n",
    "    \n",
    "    Outputs:\n",
    "    -------\n",
    "    photos_data : (dataframe) Dataframe containing photo filenames and label data.\n",
    "    \n",
    "    N : (int) Number of photos in dataset.\n",
    "    \n",
    "    labels : (array) Array of the labels associated with each photo.\n",
    "    \n",
    "    photo_filenames : (array) Array of the filenames of each photo.\n",
    "    \n",
    "    n_classes : (int) The number of unique labels in the dataset.\n",
    "    \n",
    "    h, w (int) : The dimensions (height and width) of the photos.\n",
    "    \n",
    "    '''\n",
    "    photos_data = pd.read_csv(metadata_file)#[0:240] # Read the metadata file\n",
    "    if sample != None:\n",
    "        photos_data = photos_data.sample(n=sample)\n",
    "    N = len(photos_data) # Find number of photos in dataset\n",
    "    labels = np.array(photos_data[data_col]) # Extract photo labels from desired column\n",
    "    photo_filenames = np.array(photos_data.File_name) # Extract filenames of photos\n",
    "    n_classes = len(np.unique(labels)) # Find number of unique labels\n",
    "    ex_photo = rgb2gray(plt.imread(example_photo)) # Read and convert an example photo to grayscale\n",
    "    h, w = ex_photo.shape # Extract photo shape\n",
    "    return photos_data, N, labels, photo_filenames, n_classes, h, w\n",
    "def square_transform_rotate_flatten(im, square=True, rotate=True, fft=True):\n",
    "    '''\n",
    "    Function to square, fast Fourier transform, rotate, and flatten photos. Returns flat\n",
    "    FFT image which has horizontal light-to-dark.\n",
    "    Parameters:\n",
    "    -----------\n",
    "    im : (2D array) image to be squared, transformed, rotated if necessary, and flattened.\n",
    "    \n",
    "    Outputs:\n",
    "    --------\n",
    "    psd2D (2D array) image which has been squared, transformed, rotated if necessary, and flattened.\n",
    "    '''\n",
    "    h, w = im.shape[0:2]\n",
    "    if h > w:\n",
    "        h, w = w, h\n",
    "    if fft:\n",
    "        Fs = np.fft.fft2(im) # Perform fast Fourier transform\n",
    "        F2 = np.fft.fftshift(Fs)  # Shift low frequencies to center and high frequencies to edges\n",
    "        psd2D = np.abs(F2) # Remove imaginary component\n",
    "        if square:\n",
    "            psd2D_cen = psd2D[:,int((w-h)/2):int((w-(w-h)/2)+1)]\n",
    "            mid = float(psd2D_cen[int(h/2),int(h/2)])\n",
    "            ver = sum(psd2D_cen[int((h/2)-2):int((h/2)+3), int((h/2))])-mid\n",
    "            hor = sum(psd2D_cen[int((h/2)), int((h/2)-2):int((h/2)+3)])-mid\n",
    "            if abs(ver-hor) > .1 * np.mean([ver,hor]) and (ver>hor) and rotate:\n",
    "                im = np.rot90(im)\n",
    "                im = im[int((w-h)/2):int((w-(w-h)/2)+1),:]\n",
    "            else:\n",
    "                im = im[:,int((w-h)/2):int((w-(w-h)/2)+1)]\n",
    "            Fs = np.fft.fft2(im) # Perform fast Fourier transform\n",
    "            F2 = np.fft.fftshift(Fs)  # Shift low frequencies to center and high frequencies to edges\n",
    "            psd2D = abs(F2)\n",
    "        fft_int = psd2D.astype(int) # Convert values to integers to reduce RAM usage\n",
    "    else:\n",
    "        fft_int = im.astype(int)\n",
    "    flat_psd = fft_int.flatten()\n",
    "    return flat_psd#, im_orig, im, fft_int,\n",
    "def get_fft_feat_vector(photo_filenames, photo_filepath, best_feats=None, fft=True):\n",
    "    '''\n",
    "    Function to create a feature vector of fft transformed photos.\n",
    "    \n",
    "    Inputs:\n",
    "    ------\n",
    "    photo_filenames : (array or array-like, contains strings) Array of all the filenames of photos to add to feature vector.\n",
    "    \n",
    "    photo_filepath : (str) The folder path preceding the photo filenames.\n",
    "    \n",
    "    best_feats : (list, or list-like, optional) The list of features to select from original vector.\n",
    "    \n",
    "    Outputs:\n",
    "    -------\n",
    "    fft_feature_vector : (2D array) The array of the features, size of (n_samples, n_features).\n",
    "    '''\n",
    "    start = time.time() # Initiates start time\n",
    "    fft_feature_vector = [] # Empty list for storing feature vectors\n",
    "    #print(photo_filenames)\n",
    "    for i in photo_filenames:\n",
    "        name = photo_filepath + i # Create filename to match with files in folder\n",
    "        im = rgb2gray(plt.imread(name)) # Read in photo and convert to grayscale\n",
    "        flat_psd = square_transform_rotate_flatten(im, square=False, rotate=False, fft=fft)\n",
    "        if best_feats != None: # If feature selection is occuring\n",
    "            flat_psd = flat_psd[best_feats] # Select best features\n",
    "        fft_feature_vector.append(flat_psd) # Append image vector to list\n",
    "    fft_feature_vector = np.array(fft_feature_vector) # Convert list to array\n",
    "    Runtime = time.time()-start # Calculate total runtime\n",
    "    print(\"Time to make feature vectors:\", Runtime)\n",
    "    print(sys.getsizeof(fft_feature_vector)) # Print size of feature vectors\n",
    "    return fft_feature_vector\n",
    "\n",
    "def estimate_best_forest(train_vectors, train_labels, test_vectors, test_labels, stop_score, stop_time, thresh_score=0, extract_features=False, pickled_classifier=\"hyphal_image_RF_classifier2.obj\"):\n",
    "    '''\n",
    "    Function to find best Random Forest Classifier through repeated generation of classifiers.\n",
    "    \n",
    "    Inputs:\n",
    "    -------\n",
    "    train_vectors : A two dimensional array of the features of a dataset to classify after being split into training\n",
    "    and testing data.\n",
    "    \n",
    "    train_labels : A one dimensional array of the labels corresponding to the features in the training dataset.\n",
    "    \n",
    "    test_vectors : A two dimensional array of the features of a dataset to classify after being split into training\n",
    "    and testing data.\n",
    "    \n",
    "    test_labels : A one dimensional array of the labels corresponding to the features in the testing dataset.\n",
    "    \n",
    "    stop_score : (float) The weighted f1-score which if achieved stops the generation of new classifiers.\n",
    "    \n",
    "    stop_time : (integer) The maximum time for whch new classifiers are generated, in seconds.\n",
    "    \n",
    "    thresh_score : (float) The threshold f1-score beyond which the features of a classifier are saved.\n",
    "    \n",
    "    extract_features : (=True, Boolean, optional) Whether to save best features and return them as output.\n",
    "    \n",
    "    pickled_classifier : (=\"hyphal_image_RF_classifier2.obj\", string) File location to store classifier object\n",
    "    \n",
    "    Outputs:\n",
    "    -------\n",
    "    clf : RandomForestClassifier object wih best score of those generated.\n",
    "    \n",
    "    f_scores : List of all the classifiers' weighted f1-scores.\n",
    "    \n",
    "    best_feats : List of the features used by classifiers beyond the threshold score.\n",
    "    '''\n",
    "    start = time.time() # Initiates start time\n",
    "    Runtime = 0 # Assigns Runtime variable to zero\n",
    "    f_scores_int = [] # Creates empty list for storing f1-scores\n",
    "    best_feats = [] # Creates empty list to store features of best classifiers\n",
    "    clf = RandomForestClassifier() # Creates instance of classifier object\n",
    "    #clf = clf.fit(train_vectors, train_labels) # Fits the classifier object\n",
    "    #pred_labels = clf.predict(test_vectors) # Predicts labels for the testing set\n",
    "    best_score = 0 #f1_score(test_labels, pred_labels, average='weighted') # Assigns initial best f1-score\n",
    "    while best_score < stop_score and Runtime < stop_time:\n",
    "        # Generates classifiers while score is below stop_score and Runtime is less than stop_time\n",
    "        clf_temp = clf.fit(train_vectors, train_labels) # Finds a new random forest\n",
    "        pred_labels = clf_temp.predict(test_vectors) # Predicts labels\n",
    "        temp_score = f1_score(test_labels, pred_labels, average='weighted') # Determines score of new classifier\n",
    "        f_scores_int.append(temp_score) # Append score to list\n",
    "        if temp_score > best_score: # If score is better than the previous best\n",
    "            clf = clf_temp # Temporary classifier saved as new classifier\n",
    "            pickle.dump(clf_temp, open(pickled_classifier, \"wb\")) # Store new classifier\n",
    "            best_score = temp_score # Set new best score\n",
    "            print(best_score, Runtime) # Print the score and runtime\n",
    "        if temp_score > thresh_score and extract_features: # If score is above thresh_score and extract_features is true\n",
    "            best_feats.extend(extract_imp_feats(clf_temp)) # Append best features to list\n",
    "        Runtime = time.time()-start # Calculate total runtime\n",
    "    clf = pickle.load(open(pickled_classifier, \"rb\")) # Load best classifier\n",
    "    pred_labels = clf.predict(test_vectors) # Predicts the labels using best classifier\n",
    "    print(classification_report(test_labels, pred_labels)) # Prints the classification report\n",
    "    print(confusion_matrix(test_labels, pred_labels)) # Prints the confusion matrix\n",
    "    if extract_features: # If extract features is true\n",
    "        return clf, f_scores_int, best_feats # Returns the classifier, f1-score list, and best features\n",
    "    else:\n",
    "        return clf, f_scores_int # Returns the classifier and f1-score list\n",
    "\n",
    "def extract_imp_feats(clf):\n",
    "    '''\n",
    "    Function to extract the important features of a classifier and return their indexes.\n",
    "    \n",
    "    Inputs:\n",
    "    ------\n",
    "    clf : RandomForestClassifier object\n",
    "    \n",
    "    Outputs:\n",
    "    -------\n",
    "    imp_feats_ind : (list) indices of important features\n",
    "    '''\n",
    "    feat_imps = clf.feature_importances_ # store all feature importances of the classifier\n",
    "    imp_feats = np.sum(feat_imps > 0) # Obtains the number of non-zero feature importances\n",
    "    imp_feat_indices = np.argsort(feat_imps)[-(imp_feats-10):] # Sort and extract the most important feature indexes\n",
    "    return imp_feat_indices\n",
    "\n",
    "def plot_important_feats(best_feats, h, w, plot_type='scatter'):\n",
    "    '''\n",
    "    Plotting function to plot indexes of a flattened array. Plots as either scatterplot or 2D histogram.\n",
    "    \n",
    "    Inputs:\n",
    "    ------\n",
    "    best_feats : (array, or array-like) Array of the indexes of points to plot.\n",
    "    \n",
    "    h, w : (int) Shape of pre-flattened array.\n",
    "    \n",
    "    plot_type : (str, optional) Type of plot to display. Either 'scatter' or 'hist'.\n",
    "    '''\n",
    "    best_feats_array = np.array(best_feats) # Converts input to array\n",
    "    # Acquires coordinates for given indexes\n",
    "    x = np.floor(best_feats_array/h) # Round down to find column index\n",
    "    y = np.mod(best_feats_array, w) # Modulus to find row index\n",
    "    if plot_type == 'scatter': # Plots a scatterplot\n",
    "        plt.scatter(x, y, s=1)\n",
    "    elif plot_type == 'hist': # Plots a 2D histogram\n",
    "        plt.hist2d(x, y, bins=50)\n",
    "        \n",
    "def get_f_score_stats(f_scores, percentile = 95):\n",
    "    '''\n",
    "    Function for displaying statistics and histogram of a distribution.\n",
    "    \n",
    "    Inputs:\n",
    "    ------\n",
    "    f_scores : (list, or list-like) List of floats of which to get summary statistics and histogram.\n",
    "    \n",
    "    percentile : (float, 0-100, optional) Percentile at which to get the threshold score.\n",
    "    \n",
    "    Outputs:\n",
    "    -------\n",
    "    thresh_score : (float) Value which falls at the specified percentile.\n",
    "    '''\n",
    "    print(\"Mean:\", np.mean(f_scores)) # Print mean of values\n",
    "    print(\"Median:\", np.median(f_scores)) # Print median of values\n",
    "    print(\"Count:\", len(f_scores)) # Print number of values\n",
    "    print(\"Standard Deviation:\", np.std(f_scores)) # Print standard deviation of values\n",
    "    thresh_score = np.percentile(f_scores, percentile)\n",
    "    print(\"Threshold score:\", thresh_score)\n",
    "    plt.hist(f_scores, bins = 30)\n",
    "    return thresh_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "photos_data, N, labels, photo_filenames, n_classes, h, w = read_photo_data(sample=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "564\n",
      "398 166\n"
     ]
    }
   ],
   "source": [
    "print(len(labels))\n",
    "a_count = 0\n",
    "m_count = 0\n",
    "for i in labels:\n",
    "    if i == 1:\n",
    "        m_count+=1\n",
    "    elif i == 2:\n",
    "        a_count += 1\n",
    "print(m_count, a_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "too many values to unpack (expected 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-cb6a3822387e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrgb2gray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'./Fungi Pics/JL037_009.tif'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mim_orig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mim\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfft_int\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflat_psd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msquare_transform_rotate_flatten\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mim\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m131\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mim_orig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: too many values to unpack (expected 4)"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26e8600eb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,12))\n",
    "im = rgb2gray(plt.imread('./Fungi Pics/JL037_009.tif'))\n",
    "im_orig, im, fft_int, flat_psd = square_transform_rotate_flatten(im)\n",
    "plt.subplot(131)\n",
    "plt.imshow(im_orig)\n",
    "\n",
    "plt.subplot(132)\n",
    "plt.imshow(im)\n",
    "\n",
    "plt.subplot(133)\n",
    "plt.imshow(np.log(fft_int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       " 2,\n",
       " 2,\n",
       " 2,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 2,\n",
       " 2,\n",
       " 2,\n",
       " 1,\n",
       " 1,\n",
       " 2,\n",
       " 1,\n",
       " 2,\n",
       " 1,\n",
       " 1,\n",
       " 2,\n",
       " 1,\n",
       " 1,\n",
       " 2,\n",
       " 1,\n",
       " 2,\n",
       " 2,\n",
       " 2,\n",
       " 1,\n",
       " 2,\n",
       " 2,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 2,\n",
       " 2,\n",
       " 2,\n",
       " 1,\n",
       " 2,\n",
       " 2,\n",
       " 1,\n",
       " 1,\n",
       " 2,\n",
       " 1]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_labels = []\n",
    "for i in labels:\n",
    "    if \"Clade\" in i:\n",
    "        new_labels.append(1)\n",
    "    else:\n",
    "        new_labels.append(2)\n",
    "labels = new_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time to make feature vectors: 155.18748426437378\n",
      "2516582512\n"
     ]
    }
   ],
   "source": [
    "fft_feature_vector = get_fft_feat_vector(photo_filenames, './Fungi Pics/')\n",
    "fft_train_vectors, fft_test_vectors, fft_train_labels, fft_test_labels = train_test_split(fft_feature_vector, \n",
    "                                                                          labels, random_state=324507, test_size=0.50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_components = 20\n",
    "fft_pca = PCA(n_components=n_components, whiten=True)\n",
    "_ = fft_pca.fit(fft_train_vectors)\n",
    "pickle.dump(fft_pca, open(\"hyphal_image_pca.obj\", \"wb\"))\n",
    "pca_fft_feature_vector = fft_pca.transform(fft_feature_vector)\n",
    "#for i in pca_fft_feature_vector:\n",
    "#    pca_fft_feature_vector = i[best_feats]\n",
    "pca_fft_train_vectors, pca_fft_test_vectors, fft_train_labels, fft_test_labels = train_test_split(pca_fft_feature_vector, \n",
    "                                                                          labels, random_state=324507, test_size=0.50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 20)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_fft_feature_vector.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pickle.dump(fft_feature_vector, open(\"fft_feature_vectors.obj\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.803125 0.4027557373046875\n",
      "0.820662768031 0.6907444000244141\n",
      "0.828812537673 4.72252345085144\n",
      "0.829630243741 10.05170226097107\n",
      "0.833970377449 25.630160570144653\n",
      "0.837781868385 26.731085777282715\n",
      "0.838753387534 36.56926155090332\n",
      "0.86875 45.124101638793945\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\julia\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\julia\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\julia\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\julia\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "C:\\Users\\julia\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.871609403255 733.3917229175568\n",
      "0.893007836046 803.5794837474823\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.89      0.99      0.94        75\n",
      "          2       0.94      0.64      0.76        25\n",
      "\n",
      "avg / total       0.90      0.90      0.89       100\n",
      "\n",
      "[[74  1]\n",
      " [ 9 16]]\n"
     ]
    }
   ],
   "source": [
    "clf, f_scores, best_feats = estimate_best_forest(pca_fft_train_vectors, fft_train_labels, pca_fft_test_vectors,\n",
    "                                    fft_test_labels, 0.99, 900, 0.83, extract_features=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 0.748537560802\n",
      "Median: 0.746612466125\n",
      "Count: 58917\n",
      "Standard Deviation: 0.0309776669647\n",
      "Threshold score: 0.798245614035\n"
     ]
    },
    {
     "data": {
      "image/png": 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642qbgd1VtRrY3dYBbgBWt8cm4B4YhAawBbgGuBrYcjQ4JElLZ9YQqKr/Bhw+rrwe2NGW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\nUscMAUnqmCEgSR0zBCSpY4aAJHXs/wF8GcBrYsrbmgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d04feee710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thresh_score = get_f_score_stats(f_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.777665806307 22.1565420627594\n",
      "0.824872288538 40.83571815490723\n",
      "0.829103177524 340.2543637752533\n",
      "0.832345795495 1643.8391082286835\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.90      0.86      0.88        71\n",
      "          2       0.69      0.76      0.72        29\n",
      "\n",
      "avg / total       0.84      0.83      0.83       100\n",
      "\n",
      "[[61 10]\n",
      " [ 7 22]]\n"
     ]
    }
   ],
   "source": [
    "clf, f_scores, best_feats = estimate_best_forest(fft_train_vectors, fft_train_labels, fft_test_vectors,\n",
    "                                    fft_test_labels, 0.99, 3600, thresh_score, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fm9lj4Wdt22s+E8r9lJldUFE5nzWz74WyPBKWLTCzXWZ2IPyeH5abmd0WyrjP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\nGk4NgYhIw6khEBFpODUEIiIN9/8BPVsF1i9Qc9gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b64b31b4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_important_feats(best_feats, h, h, 'hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(best_feats, open(\"best_features_mm.obj\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1517"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(best_feats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf, best_feats = pickle.load(open(\"hyphal_image_RF_classifier2.obj\", 'rb')), pickle.load(open(\"best_features.obj\", 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time to make feature vectors: 407.09626173973083\n",
      "3422464\n"
     ]
    }
   ],
   "source": [
    "'''\n",
    "photos_data, N, labels, photo_filenames, n_classes, h, w = read_photo_data()\n",
    "new_labels = []\n",
    "for i in labels:\n",
    "    if \"Mucor\" in i:\n",
    "        new_labels.append(\"Mucor\")\n",
    "    else:\n",
    "        new_labels.append(\"Mortierella\")\n",
    "labels = new_labels\n",
    "'''\n",
    "photos_data, N, labels, photo_filenames, n_classes, h, w = read_photo_data()\n",
    "fft_feature_vector = get_fft_feat_vector(photo_filenames, './Fungi Pics/', best_feats)\n",
    "fft_train_vectors, fft_test_vectors, fft_train_labels, fft_test_labels = train_test_split(fft_feature_vector, \n",
    "                                                                          labels, random_state=324507, test_size=0.50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.79419463235 0.9822211265563965\n",
      "0.798741905643 5.699821472167969\n",
      "0.818755601842 7.888740062713623\n",
      "0.826153332379 156.49126148223877\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.84      0.93      0.89       195\n",
      "          2       0.80      0.61      0.69        87\n",
      "\n",
      "avg / total       0.83      0.83      0.83       282\n",
      "\n",
      "[[182  13]\n",
      " [ 34  53]]\n"
     ]
    }
   ],
   "source": [
    "clf, f_scores = estimate_best_forest(fft_train_vectors, fft_train_labels, fft_test_vectors,\n",
    "                                    fft_test_labels, 0.99, 600, 0, extract_features = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 0.751174384931\n",
      "Median: 0.751521553242\n",
      "Count: 11039\n",
      "Standard Deviation: 0.0227336305354\n",
      "Threshold score: 0.787003504522\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.78700350452215828"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAD8CAYAAAB+UHOxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b64b12a908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_f_score_stats(f_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.7804878   0.7804878   0.8         0.71794872  0.79487179]\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(clf, fft_feature_vector, labels, cv=5)\n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pickle.dump(clf, open(\"hyphal_image_RF_classifier3.obj\", 'wb'))\n",
    "#pickle.dump(best_feats, open(\"best_features.obj\", 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# create feature vectors without fft\n",
    "feature_vector = []\n",
    "for i in photo_filenames:\n",
    "    name = './Fungi Pics/' + i\n",
    "    im = rgb2gray(plt.imread(name))\n",
    "    flat_im = im.flatten()\n",
    "    feature_vector.append(flat_im)\n",
    "feature_vector = np.array(feature_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(173, 3145728)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_vector.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into testing and training data\n",
    "train_vectors, test_vectors, train_labels, test_labels = train_test_split(feature_vector, \n",
    "                                                                          labels, \n",
    "                                                                          random_state=1,\n",
    "                                                                          test_size=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# run principal components analysis\n",
    "n_components = 10\n",
    "pca = PCA(n_components=n_components, whiten=True)\n",
    "_ = pca.fit(train_vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting the classifier to the training set\n",
      "Best estimator found by grid search:\n",
      "SVC(C=5000.0, cache_size=200, class_weight='balanced', coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=3, gamma=0.005, kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "Runtime: 0.4732656478881836\n",
      "10              precision    recall  f1-score   support\n",
      "\n",
      "          1       0.69      0.34      0.46        32\n",
      "          2       0.50      0.81      0.62        26\n",
      "\n",
      "avg / total       0.60      0.55      0.53        58\n",
      "\n",
      "10 [[ 0  0]\n",
      " [ 0 11]]\n"
     ]
    }
   ],
   "source": [
    "# run SVM on PCS transformed images without fft\n",
    "pca_train_vectors = pca.transform(train_vectors)\n",
    "pca_test_vectors = pca.transform(test_vectors)\n",
    "start = time.time()\n",
    "print(\"Fitting the classifier to the training set\")\n",
    "param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],\n",
    "              'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }\n",
    "clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)\n",
    "clf = clf.fit(pca_train_vectors, train_labels)\n",
    "print(\"Best estimator found by grid search:\")\n",
    "print(clf.best_estimator_)\n",
    "\n",
    "#pickle.dump(clf, open(PCA_test_5comp, 'wb'))\n",
    "end = time.time()\n",
    "print(\"Runtime:\", end-start)\n",
    "pred_labels = clf.predict(pca_test_vectors)\n",
    "print(n_components, classification_report(test_labels, pred_labels))\n",
    "print(n_components, confusion_matrix(test_labels, pred_labels, labels=range(n_classes)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make feature vectors that have been transformed with fft\n",
    "def get_fft_feat_vector(photo_filepath,n_photos, select_best_features):\n",
    "    fft_feature_vector = []\n",
    "    for i in photo_filenames[:n_photos+1]:\n",
    "        name = photo_filepath + i\n",
    "        im = rgb2gray(plt.imread(name))\n",
    "        gim = rgb2gray(im)\n",
    "        Fs = np.fft.fft2(gim)\n",
    "        F2 = np.fft.fftshift(Fs) \n",
    "        psd2D = np.abs(F2)\n",
    "        fft_int = psd2D.astype(int)\n",
    "        flat_psd = fft_int.flatten()\n",
    "        if select_best_features:\n",
    "            flat_psd = flat_psd[best_feats]\n",
    "        fft_feature_vector.append(flat_psd)\n",
    "    fft_feature_vector = np.array(fft_feature_vector)\n",
    "    print(sys.getsizeof(fft_feature_vector))\n",
    "    return fft_feature_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "627096\n"
     ]
    }
   ],
   "source": [
    "fft_feature_vector = get_fft_feat_vector('./Fungi Pics/', N, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split fft vectors and labels into training and testing data\n",
    "fft_train_vectors, fft_test_vectors, fft_train_labels, fft_test_labels = train_test_split(fft_feature_vector, \n",
    "                                                                          labels, \n",
    "                                                                          random_state=324507,\n",
    "                                                                          test_size=0.50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#del fft_feature_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run PCA on fft data\n",
    "n_components = 20\n",
    "fft_pca = PCA(n_components=n_components, whiten=True)\n",
    "_ = fft_pca.fit(fft_train_vectors)\n",
    "pickle.dump(fft_pca, open(\"hyphal_image_pca.obj\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_fft_train_vectors = fft_pca.transform(fft_train_vectors)\n",
    "pca_fft_test_vectors = fft_pca.transform(fft_test_vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.832016632017 0.1354055404663086\n",
      "0.836507936508 0.22658085823059082\n",
      "0.850666666667 0.6863689422607422\n",
      "0.857544152413 5.10532021522522\n",
      "0.867942583732 6.017749309539795\n",
      "0.883616265298 11.40642523765564\n",
      "0.885354896676 48.600813150405884\n",
      "0.897817460317 204.72114658355713\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.91      0.96      0.93        70\n",
      "          2       0.88      0.77      0.82        30\n",
      "\n",
      "avg / total       0.90      0.90      0.90       100\n",
      "\n",
      "[[67  3]\n",
      " [ 7 23]]\n"
     ]
    }
   ],
   "source": [
    "clf, f_scores = estimate_best_forest(pca_fft_train_vectors, fft_train_labels, pca_fft_test_vectors,\n",
    "                                    fft_test_labels, 0.99, 600, 0, extract_features = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.795593999189 0\n",
      "0.796503496503 0.19667625427246094\n",
      "0.808 0.35795116424560547\n",
      "0.820134228188 0.5324170589447021\n",
      "0.843665768194 0.5901350975036621\n",
      "0.850666666667 1.127519130706787\n",
      "0.853014553015 11.521423578262329\n",
      "0.864510332435 12.446850538253784\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-27-56b19c771e7d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m clf, f_scores, best_feats = estimate_best_forest(pca_fft_train_vectors, fft_train_labels, pca_fft_test_vectors,\n\u001b[1;32m----> 2\u001b[1;33m                                     fft_test_labels, 0.99, 600, 0, extract_features = True)\n\u001b[0m",
      "\u001b[1;32m<ipython-input-2-cca4253c37ee>\u001b[0m in \u001b[0;36mestimate_best_forest\u001b[1;34m(train_vectors, train_labels, test_vectors, test_labels, stop_score, stop_time, thresh_score, extract_features, pickled_classifier)\u001b[0m\n\u001b[0;32m    151\u001b[0m         \u001b[1;31m# Generates classifiers while score is below stop_score and Runtime is less than stop_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    152\u001b[0m         \u001b[0mclf_temp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_vectors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_labels\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Finds a new random forest\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 153\u001b[1;33m         \u001b[0mpred_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclf_temp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_vectors\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Predicts labels\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    154\u001b[0m         \u001b[0mtemp_score\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf1_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpred_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maverage\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'weighted'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Determines score of new classifier\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    155\u001b[0m         \u001b[0mf_scores_int\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtemp_score\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Append score to list\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\ensemble\\forest.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    536\u001b[0m             \u001b[0mThe\u001b[0m \u001b[0mpredicted\u001b[0m \u001b[0mclasses\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    537\u001b[0m         \"\"\"\n\u001b[1;32m--> 538\u001b[1;33m         \u001b[0mproba\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    539\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    540\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\ensemble\\forest.py\u001b[0m in \u001b[0;36mpredict_proba\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    587\u001b[0m         Parallel(n_jobs=n_jobs, verbose=self.verbose, backend=\"threading\")(\n\u001b[0;32m    588\u001b[0m             \u001b[0mdelayed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maccumulate_prediction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mall_proba\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlock\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 589\u001b[1;33m             for e in self.estimators_)\n\u001b[0m\u001b[0;32m    590\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    591\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mproba\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mall_proba\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m    777\u001b[0m             \u001b[1;31m# was dispatched. In particular this covers the edge\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    778\u001b[0m             \u001b[1;31m# case of Parallel used with an exhausted iterator.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 779\u001b[1;33m             \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    780\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    781\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    623\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    624\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 625\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    626\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    627\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    586\u001b[0m         \u001b[0mdispatch_timestamp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    587\u001b[0m         \u001b[0mcb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBatchCompletionCallBack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdispatch_timestamp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 588\u001b[1;33m         \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    589\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    590\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    109\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    110\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 111\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    112\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    113\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    330\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    331\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 332\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    333\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    334\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 131\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 131\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\ensemble\\forest.py\u001b[0m in \u001b[0;36maccumulate_prediction\u001b[1;34m(predict, X, out, lock)\u001b[0m\n\u001b[0;32m    381\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    382\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0maccumulate_prediction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlock\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 383\u001b[1;33m     \u001b[0mprediction\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    384\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mlock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    385\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\tree\\tree.py\u001b[0m in \u001b[0;36mpredict_proba\u001b[1;34m(self, X, check_input)\u001b[0m\n\u001b[0;32m    820\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'tree_'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    821\u001b[0m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 822\u001b[1;33m         \u001b[0mproba\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    823\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    824\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "clf, f_scores, best_feats = estimate_best_forest(pca_fft_train_vectors, fft_train_labels, pca_fft_test_vectors,\n",
    "                                    fft_test_labels, 0.99, 600, 0, extract_features = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting the classifier to the training set\n",
      "Best estimator found by grid search:\n",
      "SVC(C=50000.0, cache_size=200, class_weight='balanced', coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=3, gamma=0.0001, kernel='rbf',\n",
      "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
      "  tol=0.001, verbose=False)\n",
      "Runtime: 0.8051292896270752\n",
      "20              precision    recall  f1-score   support\n",
      "\n",
      "          1       0.95      0.76      0.85        72\n",
      "          2       0.60      0.89      0.71        28\n",
      "\n",
      "avg / total       0.85      0.80      0.81       100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#run SVM on PCA transformed fft data\n",
    "pca_fft_train_vectors = fft_pca.transform(fft_train_vectors)\n",
    "pca_fft_test_vectors = fft_pca.transform(fft_test_vectors)\n",
    "start = time.time()\n",
    "print(\"Fitting the classifier to the training set\")\n",
    "param_grid = {'C': [10, 1e2, 1e3, 5e3, 1e4, 5e4, 1e5],\n",
    "              'gamma': [0.00001, 0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }\n",
    "clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)\n",
    "clf = clf.fit(pca_fft_train_vectors, fft_train_labels)\n",
    "print(\"Best estimator found by grid search:\")\n",
    "print(clf.best_estimator_)\n",
    "\n",
    "pickle.dump(clf, open(\"hyphal_image_classifier.obj\", \"wb\"))\n",
    "end = time.time()\n",
    "print(\"Runtime:\", end-start)\n",
    "pred_labels = clf.predict(pca_fft_test_vectors)\n",
    "print(n_components, classification_report(fft_test_labels, pred_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Run Random Forest Classifier to find best f1-score\n",
    "def estimate_best_forest(train_vectors, train_labels, test_vectors, test_labels, stop_score, stoptime, thresh_score, extract_features=True):\n",
    "    start = time.time()\n",
    "    Runtime = 0\n",
    "    f_scores_int = []\n",
    "    clf2 = RandomForestClassifier()\n",
    "    clf2 = clf2.fit(train_vectors, train_labels)\n",
    "    pred_labels2 = clf2.predict(test_vectors)\n",
    "    best_score = f1_score(test_labels, pred_labels2, average='weighted')\n",
    "    best_feats = []\n",
    "    while f1_score(test_labels, pred_labels2) < stop_score and Runtime < stoptime:\n",
    "        clf_temp = clf2.fit(train_vectors, train_labels)\n",
    "        pred_labels2 = clf_temp.predict(test_vectors)\n",
    "        temp_score = f1_score(test_labels, pred_labels2, average='weighted')\n",
    "        f_scores_int.append(temp_score)\n",
    "        if temp_score > best_score:\n",
    "            clf2 = clf_temp\n",
    "            pickle.dump(clf_temp, open(\"hyphal_image_RF_classifier2.obj\", \"wb\"))\n",
    "            best_score = temp_score\n",
    "            print(best_score, Runtime)\n",
    "        if temp_score > thresh_score and extract_features:\n",
    "            best_feats.extend(extract_imp_feats(clf_temp))\n",
    "        end = time.time()\n",
    "        Runtime = end-start\n",
    "    clf2 = pickle.load(open(\"hyphal_image_RF_classifier2.obj\", 'rb'))\n",
    "    pred_labels2 = clf2.predict(test_vectors)\n",
    "    print(classification_report(test_labels, pred_labels2))\n",
    "    plt.hist(f_scores_int)\n",
    "    if extract_features:\n",
    "        return f_scores_int, best_feats\n",
    "    else:\n",
    "        return f_scores_int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.910995670996 0\n",
      "0.912087912088 1.0086219310760498\n",
      "0.921589688507 5.4488325119018555\n",
      "0.922616374794 11.564260244369507\n",
      "0.934065934066 23.060418128967285\n"
     ]
    }
   ],
   "source": [
    "f_scores_int = estimate_best_forest(fft_train_vectors, fft_train_labels, fft_test_vectors, fft_test_labels, 0.99, 600, 0, extract_features = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.98      0.95      0.97        65\n",
      "          2       0.89      0.96      0.93        26\n",
      "\n",
      "avg / total       0.96      0.96      0.96        91\n",
      "\n",
      "[[62  3]\n",
      " [ 1 25]]\n"
     ]
    }
   ],
   "source": [
    "clf2 = pickle.load(open(\"hyphal_image_RF_classifier2.obj\", 'rb'))\n",
    "pred_labels = clf2.predict(fft_test_vectors)\n",
    "print(classification_report(fft_test_labels, pred_labels))\n",
    "print(confusion_matrix(fft_test_labels, pred_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0260081017495\n",
      "0.854380850084\n",
      "32772\n",
      "11408\n",
      "3.28804841994\n"
     ]
    }
   ],
   "source": [
    "# data on distribution of f1 scores\n",
    "print(np.std(f_scores_int))\n",
    "print(np.median(f_scores_int))\n",
    "print(len(f_scores_int))\n",
    "print(np.argmax(f_scores_int))\n",
    "print((0.94 - np.mean(f_scores_int))/np.std(f_scores_int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(<1x90 sparse matrix of type '<class 'numpy.int64'>'\n",
      "\twith 32 stored elements in Compressed Sparse Row format>, array([ 0,  9, 18, 27, 38, 47, 56, 67, 76, 83, 90], dtype=int32))\n"
     ]
    }
   ],
   "source": [
    "test_pic = np.reshape(fft_feature_vector[0], (1,-1))\n",
    "print(clf2.decision_path(test_pic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Distribution of Random Forest Classifiers')"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JXwf+AlyQt78AuDgPfj9BupKKiFgoaRZwL7AKOC4iVjcwbjMzq6GRV1XdBexZoXwxabyj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27582c59c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(f_scores_int, bins=50);\n",
    "plt.xlabel(\"F1 score\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.title(\"Distribution of Random Forest Classifiers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(clf2, open(\"hyphal_image_RF_classifier_updated.obj\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.897817460317\n",
      "0.756190830297\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.91      0.96      0.93        70\n",
      "          2       0.88      0.77      0.82        30\n",
      "\n",
      "avg / total       0.90      0.90      0.90       100\n",
      "\n",
      "[[67  3]\n",
      " [ 7 23]]\n"
     ]
    }
   ],
   "source": [
    "clf2 = pickle.load(open(\"hyphal_image_RF_classifier2.obj\", 'rb'))\n",
    "\n",
    "pred_labels = clf2.predict(pca_fft_test_vectors)\n",
    "print(f1_score(fft_test_labels, pred_labels, average='weighted'))\n",
    "print(matthews_corrcoef(fft_test_labels, pred_labels))\n",
    "print(classification_report(fft_test_labels, pred_labels))\n",
    "print(confusion_matrix(fft_test_labels, pred_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 0.782680330598\n",
      "Median: 0.78407523511\n",
      "Count: 37470\n",
      "Standard Deviation: 0.0319622409788\n",
      "Threshold score: 0.832016632017\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.83201663201663212"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d5ae8752e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_f_score_stats(f_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def extract_imp_feats(clf):\n",
    "    '''\n",
    "    Function to extract the important features of a classifier and return their indexes.\n",
    "    Inputs:\n",
    "    clf, a RandomForestClassifier object\n",
    "    Outputs:\n",
    "    indices of important features\n",
    "    '''\n",
    "    feat_imps = clf.feature_importances_\n",
    "    imp_feats = np.sum(feat_imps > 0)\n",
    "    imp_feat_indices = np.argsort(feat_imps)[-imp_feats:]\n",
    "    return list(imp_feat_indices)\n",
    "\n",
    "def get_coords(index, h, w):\n",
    "    x = np.floor(index/h)\n",
    "    y = np.mod(index, w)\n",
    "    return np.vstack((x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def plot_important_feats(best_feats, clf, h, w, plot_type='scatter'):\n",
    "    best_feats_array = np.array(best_feats)\n",
    "    sup_feats = best_feats_array[extract_imp_feats(clf)]\n",
    "    coords = get_coords(best_feats_array, h, w)\n",
    "    im = np.zeros(h*w)\n",
    "    im = im.reshape((h,w))\n",
    "    if plot_type == 'scatter':\n",
    "        plt.scatter(coords[0],coords[1], s=1)\n",
    "    elif plot_type == 'hist':\n",
    "        plt.hist2d(coords[0],coords[1], bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Z5ZwIzMwq50RgZlY5JwIzs8o5EZiZVc6JwMysck4EZmaVcyIwM6ucE4GZWeWcCMzMKtf1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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x275979f4438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_important_feats(best_feats, clf2, h, w, 'hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(clf2, open(\"hyphal_image_RF_classifier2.obj\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c33e93f6d8>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cYAq37Gbswwv5ZO+RoMs6JSu27mHSo0vo0CqV2ZNH0KFlatAlNSgKfZE4cuWQ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\nSBxR6IuIxJH/D4KIJpzkMQ8QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c33e7c7ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iterations = [1,2,3,4,5]\n",
    "f1_scores_perc = []\n",
    "for i in iterations:\n",
    "    clf5 = neighbors.KNeighborsClassifier(n_neighbors=i)\n",
    "    clf5.fit(fft_train_vectors, fft_train_labels)\n",
    "    pred_labels5 = clf5.predict(fft_test_vectors)\n",
    "    f1_scores_perc.append(f1_score(fft_test_labels, pred_labels5, average='weighted'))\n",
    "#print(classification_report(fft_test_labels, pred_labels))\n",
    "plt.plot(iterations, f1_scores_perc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Principal Components Analysis')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xrqj93C0llUn9B7R7MgDokZbGveecT++MDNIjEdLCYfp368YD532BtMje4izV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f42e0d9cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting of principle components\n",
    "n1 = 2\n",
    "n2 = 3\n",
    "plt.scatter(pca_fft_test_vectors[:,n1], pca_fft_test_vectors[:,n2], c=test_labels, cmap='viridis', vmin = 0, vmax = 2)\n",
    "plt.scatter(pca_fft_train_vectors[:,n1], pca_fft_train_vectors[:,n2], c=train_labels, cmap='viridis', vmin=0, vmax = 2)\n",
    "plt.xlabel('PC'+str(n1+1))\n",
    "plt.ylabel('PC'+str(n2+1))\n",
    "plt.title(\"Principal Components Analysis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def photo_transform(im_name):\n",
    "    '''\n",
    "    Reads the image file and applies fast Fourier transform, then flattens it for use by the PCA and SVM algorithms.\n",
    "    Input: image name, such as \"./Fungi Pics/JU-15B.tif\".\n",
    "    Output: flattened and 2D array of frequency space\n",
    "    '''\n",
    "    im = imread(im_name, flatten=True) # read image and converts it to gray\n",
    "    Fs = np.fft.fft2(im) # run fast Fourier transform on gray image\n",
    "    F2 = np.fft.fftshift(Fs) # move the zero frequency component to the center\n",
    "    psd2D = np.abs(F2) # remove imaginary values\n",
    "    flat_psd = psd2D.flatten() # flatten the array\n",
    "    flat_psd = flat_psd.reshape((1, -1)) # make it 2D for PCA and SVM\n",
    "    return flat_psd\n",
    "def photo_pred(flat_psd):\n",
    "    '''\n",
    "    Makes a prediction of which class the fungi is in when given a transformed image.\n",
    "    Input: flattened 2D fft image from photo_transform\n",
    "    Output: class prediction\n",
    "    '''\n",
    "    pca = pickle.load(open(\"hyphal_image_pca.obj\", 'rb')) # load up-to-data PCA and SVM classifier\n",
    "    clf = pickle.load(open(\"hyphal_image_classifier.obj\", 'rb'))\n",
    "    pca_psd = pca.transform(flat_psd) # transform with the PCA\n",
    "    pred_label = clf.predict(pca_psd) # predict with the SVM\n",
    "    if pred_label == 1: # return prediction decision\n",
    "        return \"Zygomycete\"\n",
    "    if pred_label == 2:\n",
    "        return \"Ascomycete or Basidiomycete\"\n",
    "def main(im_name):\n",
    "    flat_psd = photo_transform(im_name)\n",
    "    pred_label = photo_pred(flat_psd)\n",
    "    print(pred_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1536, 2048, 3)\n",
      "Vert: 2308662.48723\n",
      "Horz: 3353480.50563\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'FFT Image')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f4LkpMFqidsi3JmwYZDjVwgvPPM/pfa/ziZdPXD6rvOdiz6l1ejxwbEbIiVm2n5eNwUlg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nzLXBPuaYMZEhC0teeLDCkYXHckZb5+VQUhzkHtQx6EemqWIcmANqvMqKSibriaCypFesCVLJ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hIgJNpo1AGLKhyC3wrGl1C6VcQUccdaQARXKpYWiMpoTtHguR/sL5XPzw+UBIc7W9m4iFOA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\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KRmFJlZ4ETXe4zr+zfnDEB04npaCHkLRTxCmcQTJFg1cOFpkNQI0jPtNg9d1M3XwAMlMx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\n8YnoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21320ac2be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "im = plt.imread(\"./Fungi Pics/JU-25.tif\")\n",
    "print(im.shape)\n",
    "gim = rgb2gray(im) # Read in photo and convert to grayscale\n",
    "Fs = np.fft.fft2(gim) # Perform fast Fourier transform\n",
    "F2 = np.fft.fftshift(Fs)  # Shift low frequencies to center and high frequencies to edges\n",
    "psd2D = np.abs(F2) # Remove imaginary component\n",
    "fft_int = psd2D.astype(int) # Convert values to integers to reduce RAM usage\n",
    "flat_psd = fft_int.flatten() # flatten the array\n",
    "flat_psd = flat_psd.reshape((1, -1))\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.subplot(121)\n",
    "plt.imshow(im)\n",
    "plt.axis('off')\n",
    "plt.title(\"Original\")\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.imshow(np.log(psd2D)[766:771, 1022:1027], cmap='viridis')\n",
    "print(\"Vert:\", sum(psd2D[766:771, 1024]))\n",
    "print(\"Horz:\", sum(psd2D[768, 1022:1027]))\n",
    "plt.axis()\n",
    "plt.title(\"FFT Image\")\n",
    "#clf = pickle.load(open('hyphal_image_RF_classifier.obj', 'rb'))\n",
    "#plt.suptitle(\"Predicted Class: \" + str(clf.predict(flat_psd)), y=0.8)\n",
    "#plt.savefig(\"hyphal_image_classification.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1536, 1536, 3)\n",
      "foo\n",
      "(1536, 1536)\n",
      "Vert: 1538285.63104\n",
      "Horz: 1727553.32342\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'FFT Image')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GALv7K4lvs0EuCbIodvdX+JKgjzlsilTa/buvVF+d66ap681+6uqbOuebQu/C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\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g9zT79pmSk17OF8t3SWatGpgXD+raQ6k11y8IgDUkZVRZNT9gNRXVcrdd0tAJKK+EFgRT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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21311a156a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "im = plt.imread(\"./Fungi Pics/JU-50.tif\")[:,256:1792]\n",
    "print(im.shape)\n",
    "gim = rgb2gray(im)# Read in photo and convert to grayscale\n",
    "Fs = np.fft.fft2(gim) # Perform fast Fourier transform\n",
    "F2 = np.fft.fftshift(Fs)  # Shift low frequencies to center and high frequencies to edges\n",
    "psd2D = np.abs(F2) # Remove imaginary component\n",
    "fft_int = psd2D.astype(int) # Convert values to integers to reduce RAM usage\n",
    "ver = sum(psd2D[766:771, 768])\n",
    "hor = sum(psd2D[768, 766:771])\n",
    "if abs(ver-hor) > .1 * np.mean([ver,hor]) and (ver>hor):\n",
    "    print(\"foo\")\n",
    "    psd2D = np.rot90(psd2D)\n",
    "flat_psd = fft_int.flatten() # flatten the array\n",
    "flat_psd = flat_psd.reshape((1, -1))\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.subplot(121)\n",
    "plt.imshow(gim)\n",
    "plt.axis('off')\n",
    "plt.title(\"Original\")\n",
    "print(psd2D.shape)\n",
    "plt.subplot(122)\n",
    "plt.imshow(np.log(psd2D)[765:770, 766:771], cmap='viridis')\n",
    "print(\"Vert:\", sum(psd2D[765:770, 768]))\n",
    "print(\"Horz:\", sum(psd2D[767, 766:771]))\n",
    "plt.axis()\n",
    "plt.title(\"FFT Image\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def square_transform_rotate_flatten(im):\n",
    "    '''\n",
    "    Function to square, fast Fourier transform, rotate, and flatten photos. Returns flat\n",
    "    FFT image which has horizontal light-to-dark.\n",
    "    Parameters:\n",
    "    -----------\n",
    "    im : (2D array) image to be squared, transformed, rotated if necessary, and flattened.\n",
    "    \n",
    "    Outputs:\n",
    "    --------\n",
    "    psd2D (2D array) image which has been squared, transformed, rotated if necessary, and flattened.\n",
    "    '''\n",
    "    h, w = im.shape[0:2]\n",
    "    if h > w:\n",
    "        h, w = w, h\n",
    "    Fs = np.fft.fft2(im) # Perform fast Fourier transform\n",
    "    F2 = np.fft.fftshift(Fs)  # Shift low frequencies to center and high frequencies to edges\n",
    "    psd2D = np.abs(F2) # Remove imaginary component\n",
    "    psd2D = psd2D[:,(w-h)/2:(w-(w-h)/2)+1]\n",
    "    ver = sum(psd2D[(h/2)-2:(h/2)+3, (h/2)])\n",
    "    hor = sum(psd2D[(h/2), (h/2)-2:(h/2)+3])\n",
    "    if abs(ver-hor) > .1 * np.mean([ver,hor]) and (ver>hor):\n",
    "        psd2D = np.rot90(psd2D)\n",
    "    fft_int = psd2D.astype(int) # Convert values to integers to reduce RAM usage\n",
    "    flat_psd = fft_int.flatten()\n",
    "    return flat_psd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "#for i in [10, 20, 30, 40, 50, 60, 70, 100]:\n",
    "    #clf = MLPClassifier(solver='lbfgs', alpha=1e-2, hidden_layer_sizes=(i))\n",
    "    #start = time.time()\n",
    "    #clf.fit(pca_fft_train_vectors, fft_train_labels)\n",
    "    #pred_labels = clf.predict(pca_fft_test_vectors)\n",
    "    #print(time.time()-start)\n",
    "    #print(i, j)\n",
    "    #print(classification_report(fft_test_labels, pred_labels))\n",
    "    #print(confusion_matrix(fft_test_labels, pred_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "class rand_for_base():\n",
    "    def __init__(self, runtime=300):\n",
    "        self.clf_base = RandomForestClassifier()\n",
    "        self.f_scores = []\n",
    "        self.runtime = runtime\n",
    "class pca_iter_rand_for(rand_for_base):\n",
    "    def fit(self, train_vec, train_labels, test_vec, test_labels):\n",
    "        n_components = 20\n",
    "        self.fft_pca = PCA(n_components=n_components, whiten=True)\n",
    "        _ = self.fft_pca.fit(train_vec)\n",
    "        pca_train_vector = self.fft_pca.transform(train_vec)\n",
    "        pca_test_vector = self.fft_pca.transform(test_vec)\n",
    "        self.clf_base, self.f_scores = estimate_best_forest(pca_train_vector, train_labels, pca_test_vector, test_labels, 0.99, self.runtime)\n",
    "        return self.clf_base\n",
    "    def predict(self, x):\n",
    "        self.pca_x = self.fft_pca.transform(x)\n",
    "        return self.clf_base.predict(self.pca_x)\n",
    "class feat_select_rand_for(rand_for_base):\n",
    "    def fit(self, train_vec, train_labels, test_vec, test_labels):\n",
    "        self.best_fs = []\n",
    "        self.clf_base, self.f_scores = estimate_best_forest(train_vec, train_labels, test_vec, test_labels, 0.99, self.runtime)\n",
    "        self.threshold = get_f_score_stats(self.f_scores)\n",
    "        self.best_fs.append(max(self.f_scores))\n",
    "        self.clf_base, self.f_scores, self.best_feats = estimate_best_forest(train_vec, train_labels, test_vec, test_labels, 0.99, self.runtime*4, thresh_score=self.threshold, extract_features=True)\n",
    "        self.best_fs.append(max(self.f_scores))\n",
    "        new_train_vec, new_test_vec = [], []\n",
    "        if len(self.best_feats) > 0:\n",
    "            for i in train_vec:\n",
    "                new_train_vec.append(i[self.best_feats])\n",
    "                new_test_vec.append(i[self.best_feats])\n",
    "            self.clf_base, self.f_scores = estimate_best_forest(new_train_vec, train_labels, new_test_vec, test_labels, 0.99, self.runtime)\n",
    "            self.best_fs.append(max(self.f_scores))\n",
    "        print(max(self.best_fs))\n",
    "        return self.clf_base\n",
    "    def predict(self, x):\n",
    "        self.new_x = []\n",
    "        if len(self.best_feats) > 0:\n",
    "            for i in x:\n",
    "                self.new_x.append(i[self.best_feats])\n",
    "        else:\n",
    "            self.new_x = x\n",
    "        return self.clf_base.predict(self.new_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time to make feature vectors: 181.14914178848267\n",
      "2516582512\n",
      "Running trial RF+FFT+PCA 1\n",
      "0.6634168797953964 0\n",
      "0.7095639097744362 0.4587748050689697\n",
      "0.7299189270343308 0.5694785118103027\n",
      "0.738743961352657 1.7612946033477783\n",
      "0.7588405797101448 1.9996566772460938\n",
      "0.7685391672547636 6.511589765548706\n",
      "0.7773214285714286 14.241921424865723\n",
      "0.7878955359854235 40.48774480819702\n",
      "0.7896842105263159 44.30454349517822\n",
      "0.7965728274173807 202.69005036354065\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.75      0.92      0.82        51\n",
      "          2       0.89      0.67      0.77        49\n",
      "\n",
      "avg / total       0.82      0.80      0.80       100\n",
      "\n",
      "[[47  4]\n",
      " [16 33]]\n",
      "0.7965728274173807\n",
      "Running trial RF+FFT+PCA 2\n",
      "0.7267866951480012 0\n",
      "0.7348768472906403 0.7350354194641113\n",
      "0.7470247177296306 0.7659523487091064\n",
      "0.7647657295850067 1.2137548923492432\n",
      "0.7792914653784218 2.408560276031494\n",
      "0.7933333333333333 2.840404987335205\n",
      "0.8056760374832663 25.189847469329834\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.75      0.94      0.84        52\n",
      "          2       0.91      0.67      0.77        48\n",
      "\n",
      "avg / total       0.83      0.81      0.81       100\n",
      "\n",
      "[[49  3]\n",
      " [16 32]]\n",
      "0.8056760374832663\n",
      "Running trial RF+FFT+PCA 3\n",
      "0.7107268170426064 0\n",
      "0.7234752589182969 0.204453706741333\n",
      "0.748081841432225 0.22739195823669434\n",
      "0.7690875801037536 0.3061823844909668\n",
      "0.7897661706650471 0.39294981956481934\n",
      "0.7980295566502462 2.8663365840911865\n",
      "0.8301542494203045 4.173840522766113\n",
      "0.8473389355742298 42.24704360961914\n",
      "0.8570833333333333 152.1142783164978\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.82      0.96      0.88        55\n",
      "          2       0.94      0.73      0.83        45\n",
      "\n",
      "avg / total       0.87      0.86      0.86       100\n",
      "\n",
      "[[53  2]\n",
      " [12 33]]\n",
      "0.8570833333333333\n",
      "Running trial RF+FFT+PCA 4\n",
      "0.5741067675493905 0\n",
      "0.6592854140395125 0.19647622108459473\n",
      "0.6951515151515152 0.22240638732910156\n",
      "0.7060530295392681 0.2762629985809326\n",
      "0.7452677396497621 0.32413411140441895\n",
      "0.7880795899909557 0.9534509181976318\n",
      "0.7967676767676768 18.29071068763733\n",
      "0.7985547972701725 40.47339868545532\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.73      0.91      0.81        47\n",
      "          2       0.90      0.70      0.79        53\n",
      "\n",
      "avg / total       0.82      0.80      0.80       100\n",
      "\n",
      "[[43  4]\n",
      " [16 37]]\n",
      "0.7985547972701725\n",
      "Running trial RF+FFT+PCA 5\n",
      "0.672987012987013 0\n",
      "0.7045105779937241 0.26828598976135254\n",
      "0.7154747474747475 0.4607696533203125\n",
      "0.7285112781954888 1.2845661640167236\n",
      "0.7292703433089781 1.8610265254974365\n",
      "0.74 2.3018457889556885\n",
      "0.7477137976082807 3.96041202545166\n",
      "0.7493243919527575 6.033867120742798\n",
      "0.7582657567242072 7.1060028076171875\n",
      "0.76 13.222647905349731\n",
      "0.7687318295739348 14.647836208343506\n",
      "0.7693784405965368 16.22661590576172\n",
      "0.8001602564102563 28.685302257537842\n",
      "0.8100950095009501 57.45042681694031\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.77      0.85      0.81        47\n",
      "          2       0.85      0.77      0.81        53\n",
      "\n",
      "avg / total       0.81      0.81      0.81       100\n",
      "\n",
      "[[40  7]\n",
      " [12 41]]\n",
      "0.8100950095009501\n",
      "Mean: 0.8135964010010206\n",
      "Median: 0.8056760374832663\n",
      "Count: 5\n",
      "Standard Deviation: 0.02228074094243164\n",
      "Threshold score: 0.8476856685668567\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8476856685668567"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVLTKxSSbE3ycJKDSa6dp/+sJL/X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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29e83252f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#photos_data, N, labels, photo_filenames, n_classes, h, w = read_photo_data(sample=200)\n",
    "fft_feature_vector = get_fft_feat_vector(photo_filenames, './Fungi Pics/', fft=True)\n",
    "best_scores = []\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "for i in range(5):\n",
    "    print(\"Running trial RF+FFT+PCA\", i+1)\n",
    "    fft_train_vectors, fft_test_vectors, fft_train_labels, fft_test_labels = train_test_split(fft_feature_vector, labels, test_size=0.50)\n",
    "    clf = pca_iter_rand_for(runtime=300)\n",
    "    clf.fit(fft_train_vectors, fft_train_labels, fft_test_vectors, fft_test_labels)\n",
    "    pred_labels = clf.predict(fft_test_vectors)\n",
    "    best_scores.append(f1_score(fft_test_labels, pred_labels, average='weighted'))\n",
    "    print(f1_score(fft_test_labels, pred_labels, average='weighted'))\n",
    "get_f_score_stats(best_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time to make feature vectors: 53.11714696884155\n",
      "2516582512\n",
      "Running trial 1\n",
      "0.23843971631205674 0\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "Actinomucor       0.00      0.00      0.00         4\n",
      "    Clade 1       0.00      0.00      0.00         1\n",
      "    Clade 2       0.00      0.00      0.00         5\n",
      "    Clade 3       0.00      0.00      0.00         5\n",
      "    Clade 6       0.00      0.00      0.00         8\n",
      "    Clade 7       0.00      0.00      0.00        26\n",
      "    Clade 8       0.00      0.00      0.00        10\n",
      "      Mucor       0.41      1.00      0.58        41\n",
      "\n",
      "avg / total       0.17      0.41      0.24       100\n",
      "\n",
      "[[ 0  0  0  0  0  0  0  4]\n",
      " [ 0  0  0  0  0  0  0  1]\n",
      " [ 0  0  0  0  0  0  0  5]\n",
      " [ 0  0  0  0  0  0  0  5]\n",
      " [ 0  0  0  0  0  0  0  8]\n",
      " [ 0  0  0  0  0  0  0 26]\n",
      " [ 0  0  0  0  0  0  0 10]\n",
      " [ 0  0  0  0  0  0  0 41]]\n",
      "Mean: 0.23843971631205674\n",
      "Median: 0.23843971631205674\n",
      "Count: 3\n",
      "Standard Deviation: 0.0\n",
      "Threshold score: 0.23843971631205674\n",
      "0.2594021164021164 0\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-f44a10e76a3a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mfft_train_vectors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfft_test_vectors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfft_train_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfft_test_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfft_feature_vector\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.50\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mclf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfeat_select_rand_for\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mruntime\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m300\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfft_train_vectors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfft_train_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfft_test_vectors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfft_test_labels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m     \u001b[0mpred_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfft_test_vectors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mbest_scores\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf1_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfft_test_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpred_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maverage\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'weighted'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-15-a284aeb110f5>\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, train_vec, train_labels, test_vec, test_labels)\u001b[0m\n\u001b[0;32m     22\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mthreshold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_f_score_stats\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf_scores\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     23\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_fs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf_scores\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 24\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclf_base\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf_scores\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_feats\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mestimate_best_forest\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_vec\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_vec\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.99\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mruntime\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mthresh_score\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mthreshold\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mextract_features\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     25\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_fs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf_scores\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     26\u001b[0m         \u001b[0mnew_train_vec\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_test_vec\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-2-def3b2cd371c>\u001b[0m in \u001b[0;36mestimate_best_forest\u001b[1;34m(train_vectors, train_labels, test_vectors, test_labels, stop_score, stop_time, thresh_score, extract_features, pickled_classifier)\u001b[0m\n\u001b[0;32m    152\u001b[0m     \u001b[1;32mwhile\u001b[0m \u001b[0mbest_score\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mstop_score\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mRuntime\u001b[0m \u001b[1;33m<\u001b[0m \u001b[0mstop_time\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    153\u001b[0m         \u001b[1;31m# Generates classifiers while score is below stop_score and Runtime is less than stop_time\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 154\u001b[1;33m         \u001b[0mclf_temp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_vectors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_labels\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Finds a new random forest\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    155\u001b[0m         \u001b[0mpred_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclf_temp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_vectors\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Predicts labels\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    156\u001b[0m         \u001b[0mtemp_score\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf1_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtest_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpred_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maverage\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'weighted'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Determines score of new classifier\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\ensemble\\forest.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    326\u001b[0m                     \u001b[0mt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrees\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    327\u001b[0m                     verbose=self.verbose, class_weight=self.class_weight)\n\u001b[1;32m--> 328\u001b[1;33m                 for i, t in enumerate(trees))\n\u001b[0m\u001b[0;32m    329\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    330\u001b[0m             \u001b[1;31m# Collect newly grown trees\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m    777\u001b[0m             \u001b[1;31m# was dispatched. In particular this covers the edge\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    778\u001b[0m             \u001b[1;31m# case of Parallel used with an exhausted iterator.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 779\u001b[1;33m             \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    780\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    781\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    623\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    624\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 625\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    626\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    627\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    586\u001b[0m         \u001b[0mdispatch_timestamp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    587\u001b[0m         \u001b[0mcb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBatchCompletionCallBack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdispatch_timestamp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 588\u001b[1;33m         \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    589\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    590\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    109\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    110\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 111\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    112\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    113\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\externals\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    330\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    331\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 332\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    333\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    334\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 131\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\externals\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 131\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    133\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__len__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\ensemble\\forest.py\u001b[0m in \u001b[0;36m_parallel_build_trees\u001b[1;34m(tree, forest, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight)\u001b[0m\n\u001b[0;32m    119\u001b[0m             \u001b[0mcurr_sample_weight\u001b[0m \u001b[1;33m*=\u001b[0m \u001b[0mcompute_sample_weight\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'balanced'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    120\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 121\u001b[1;33m         \u001b[0mtree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcurr_sample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    122\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    123\u001b[0m         \u001b[0mtree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\tree\\tree.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    788\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    789\u001b[0m             \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 790\u001b[1;33m             X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[0;32m    791\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    792\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\tree\\tree.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    360\u001b[0m                                            min_impurity_split)\n\u001b[0;32m    361\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 362\u001b[1;33m         \u001b[0mbuilder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_idx_sorted\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    363\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    364\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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3STKT5G+6Ne8Afh24Isnu7nbW6oy7PN059P+7bMRe4IvVXTYiycXdshuBE7vLRnyAhU8P\nHbN67vNfAscBX+p+roNhOKb03Odm9Nzfu4BnkjwG3AP8cVU9szoTrx6/oSpJDVqLR+6S1DzjLkkN\nMu6S1CDjLkkNMu6S1CDjLkkNMu6S1CDjLkkN+l/l0rWHfKOCnQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29e80078198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "photos_data, N, labels, photo_filenames, n_classes, h, w = read_photo_data(sample=200)\n",
    "fft_feature_vector = get_fft_feat_vector(photo_filenames, './Fungi Pics/', fft=False)\n",
    "best_scores = []\n",
    "for i in range(5):\n",
    "    print(\"Running trial FSRF\", i+1)\n",
    "    fft_train_vectors, fft_test_vectors, fft_train_labels, fft_test_labels = train_test_split(fft_feature_vector, labels, test_size=0.50)\n",
    "    clf = feat_select_rand_for(runtime=300)\n",
    "    clf.fit(fft_train_vectors, fft_train_labels, fft_test_vectors, fft_test_labels)\n",
    "    pred_labels = clf.predict(fft_test_vectors)\n",
    "    best_scores.append(f1_score(fft_test_labels, pred_labels, average='weighted'))\n",
    "    print(f1_score(fft_test_labels, pred_labels, average='weighted'))\n",
    "get_f_score_stats(best_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time to make feature vectors: 144.53687930107117\n",
      "2516582512\n",
      "Running trial ANN+FFT 1\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'MLPClassifier' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-12-217e70050ef0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Running trial ANN+FFT\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mfft_train_vectors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfft_test_vectors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfft_train_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfft_test_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfft_feature_vector\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.50\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[0mclf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMLPClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msolver\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'lbfgs'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1e-2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhidden_layer_sizes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m     \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfft_train_vectors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfft_train_labels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[0mpred_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfft_test_vectors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'MLPClassifier' is not defined"
     ]
    }
   ],
   "source": [
    "#photos_data, N, labels, photo_filenames, n_classes, h, w = read_photo_data(sample=200)\n",
    "fft_feature_vector = get_fft_feat_vector(photo_filenames, './Fungi Pics/')\n",
    "best_scores = []\n",
    "for i in range(5):\n",
    "    print(\"Running trial ANN+FFT\", i+1)\n",
    "    fft_train_vectors, fft_test_vectors, fft_train_labels, fft_test_labels = train_test_split(fft_feature_vector, labels, test_size=0.50)\n",
    "    clf = clf = MLPClassifier(solver='lbfgs', alpha=1e-2, hidden_layer_sizes=60)\n",
    "    clf.fit(fft_train_vectors, fft_train_labels)\n",
    "    pred_labels = clf.predict(fft_test_vectors)\n",
    "    best_scores.append(f1_score(fft_test_labels, pred_labels, average='weighted'))\n",
    "    print(f1_score(fft_test_labels, pred_labels, average='weighted'))\n",
    "    print(time.time()-start)\n",
    "    print(classification_report(fft_test_labels, pred_labels))\n",
    "    print(confusion_matrix(fft_test_labels, pred_labels))\n",
    "get_f_score_stats(best_scores)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_scores = []\n",
    "#photos_data, N, labels, photo_filenames, n_classes, h, w = read_photo_data(sample=10)\n",
    "#fft_feature_vector = get_fft_feat_vector(photo_filenames, './Fungi Pics/')\n",
    "for i in range(5):\n",
    "    print(\"Running trial ANN+PCA+FFT\", i+1)\n",
    "    fft_train_vectors, fft_test_vectors, fft_train_labels, fft_test_labels = train_test_split(fft_feature_vector, labels, test_size=0.50)\n",
    "    n_components = 20\n",
    "    fft_pca = PCA(n_components=n_components, whiten=True)\n",
    "    _ = fft_pca.fit(fft_train_vectors)\n",
    "    pca_train_vectors = fft_pca.transform(fft_train_vectors)\n",
    "    pca_test_vectors = fft_pca.transform(fft_test_vectors)\n",
    "    clf = clf = MLPClassifier(solver='lbfgs', alpha=1e-2, hidden_layer_sizes=60)\n",
    "    clf.fit(pca_train_vectors, fft_train_labels)\n",
    "    pred_labels = clf.predict(pca_test_vectors)\n",
    "    best_scores.append(f1_score(fft_test_labels, pred_labels, average='weighted'))\n",
    "    print(f1_score(fft_test_labels, pred_labels, average='weighted'))\n",
    "    print(classification_report(fft_test_labels, pred_labels))\n",
    "    print(confusion_matrix(fft_test_labels, pred_labels))\n",
    "get_f_score_stats(best_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time to make feature vectors: 55.23473620414734\n",
      "2516582512\n",
      "Running trial ANN 1\n"
     ]
    },
    {
     "ename": "MemoryError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-15-9f60aad6a374>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mfft_train_vectors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfft_test_vectors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfft_train_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfft_test_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfft_feature_vector\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.50\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mclf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMLPClassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msolver\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'lbfgs'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1e-2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhidden_layer_sizes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfft_train_vectors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfft_train_labels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m     \u001b[0mpred_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfft_test_vectors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[0mbest_scores\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf1_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfft_test_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpred_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maverage\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'weighted'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    971\u001b[0m         \"\"\"\n\u001b[0;32m    972\u001b[0m         return self._fit(X, y, incremental=(self.warm_start and\n\u001b[1;32m--> 973\u001b[1;33m                                             hasattr(self, \"classes_\")))\n\u001b[0m\u001b[0;32m    974\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    975\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py\u001b[0m in \u001b[0;36m_fit\u001b[1;34m(self, X, y, incremental)\u001b[0m\n\u001b[0;32m    381\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msolver\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'lbfgs'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    382\u001b[0m             self._fit_lbfgs(X, y, activations, deltas, coef_grads,\n\u001b[1;32m--> 383\u001b[1;33m                             intercept_grads, layer_units)\n\u001b[0m\u001b[0;32m    384\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    385\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\sklearn\\neural_network\\multilayer_perceptron.py\u001b[0m in \u001b[0;36m_fit_lbfgs\u001b[1;34m(self, X, y, activations, deltas, coef_grads, intercept_grads, layer_units)\u001b[0m\n\u001b[0;32m    468\u001b[0m             \u001b[0miprint\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0miprint\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    469\u001b[0m             \u001b[0mpgtol\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtol\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 470\u001b[1;33m             args=(X, y, activations, deltas, coef_grads, intercept_grads))\n\u001b[0m\u001b[0;32m    471\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    472\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_unpack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moptimal_parameters\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\scipy\\optimize\\lbfgsb.py\u001b[0m in \u001b[0;36mfmin_l_bfgs_b\u001b[1;34m(func, x0, fprime, args, approx_grad, bounds, m, factr, pgtol, epsilon, iprint, maxfun, maxiter, disp, callback, maxls)\u001b[0m\n\u001b[0;32m    191\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    192\u001b[0m     res = _minimize_lbfgsb(fun, x0, args=args, jac=jac, bounds=bounds,\n\u001b[1;32m--> 193\u001b[1;33m                            **opts)\n\u001b[0m\u001b[0;32m    194\u001b[0m     d = {'grad': res['jac'],\n\u001b[0;32m    195\u001b[0m          \u001b[1;34m'task'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'message'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\scipy\\optimize\\lbfgsb.py\u001b[0m in \u001b[0;36m_minimize_lbfgsb\u001b[1;34m(fun, x0, args, jac, bounds, disp, maxcor, ftol, gtol, eps, maxfun, maxiter, iprint, callback, maxls, **unknown_options)\u001b[0m\n\u001b[0;32m    260\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'length of x0 != length of bounds'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m     \u001b[1;31m# unbounded variables must use None, not +-inf, for optimizer to work properly\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m     \u001b[0mbounds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mNone\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0ml\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;33m-\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minf\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mu\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minf\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mu\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mu\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mbounds\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    263\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    264\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mdisp\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\py36\\lib\\site-packages\\scipy\\optimize\\lbfgsb.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    260\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'length of x0 != length of bounds'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    261\u001b[0m     \u001b[1;31m# unbounded variables must use None, not +-inf, for optimizer to work properly\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 262\u001b[1;33m     \u001b[0mbounds\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mNone\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0ml\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;33m-\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minf\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mu\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minf\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mu\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mu\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mbounds\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    263\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    264\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mdisp\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMemoryError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "fft_feature_vector = get_fft_feat_vector(photo_filenames, './Fungi Pics/', fft=False)\n",
    "best_scores = []\n",
    "for i in range(5):\n",
    "    print(\"Running trial ANN\", i+1)\n",
    "    fft_train_vectors, fft_test_vectors, fft_train_labels, fft_test_labels = train_test_split(fft_feature_vector, labels, test_size=0.50)\n",
    "    clf = clf = MLPClassifier(solver='lbfgs', alpha=1e-2, hidden_layer_sizes=60)\n",
    "    clf.fit(fft_train_vectors, fft_train_labels)\n",
    "    pred_labels = clf.predict(fft_test_vectors)\n",
    "    best_scores.append(f1_score(fft_test_labels, pred_labels, average='weighted'))\n",
    "    print(f1_score(fft_test_labels, pred_labels, average='weighted'))\n",
    "    print(time.time()-start)\n",
    "    print(classification_report(fft_test_labels, pred_labels))\n",
    "    print(confusion_matrix(fft_test_labels, pred_labels))\n",
    "get_f_score_stats(best_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fft_feature_vector = get_fft_feat_vector(photo_filenames, './Fungi Pics/', fft=False)\n",
    "best_scores = []\n",
    "for i in range(5):\n",
    "    print(\"Running trial ANN+PCA\", i+1)\n",
    "    fft_train_vectors, fft_test_vectors, fft_train_labels, fft_test_labels = train_test_split(fft_feature_vector, labels, test_size=0.50)\n",
    "    n_components = 20\n",
    "    fft_pca = PCA(n_components=n_components, whiten=True)\n",
    "    _ = fft_pca.fit(fft_train_vectors)\n",
    "    pca_train_vectors = fft_pca.transform(fft_train_vectors)\n",
    "    pca_test_vectors = fft_pca.transform(fft_test_vectors)\n",
    "    clf = clf = MLPClassifier(solver='lbfgs', alpha=1e-2, hidden_layer_sizes=60)\n",
    "    clf.fit(pca_train_vectors, fft_train_labels)\n",
    "    pred_labels = clf.predict(pca_test_vectors)\n",
    "    best_scores.append(f1_score(fft_test_labels, pred_labels, average='weighted'))\n",
    "    print(f1_score(fft_test_labels, pred_labels, average='weighted'))\n",
    "    print(classification_report(fft_test_labels, pred_labels))\n",
    "    print(confusion_matrix(fft_test_labels, pred_labels))\n",
    "get_f_score_stats(best_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_scores = []\n",
    "for i in range(5):\n",
    "    print(\"Running trial SVM+PCA\", i+1)\n",
    "    fft_train_vectors, fft_test_vectors, fft_train_labels, fft_test_labels = train_test_split(fft_feature_vector, labels, test_size=0.50)\n",
    "    start = time.time()\n",
    "    n_components = 20\n",
    "    fft_pca = PCA(n_components=n_components, whiten=True)\n",
    "    _ = fft_pca.fit(fft_train_vectors)\n",
    "    pca_train_vectors = fft_pca.transform(fft_train_vectors)\n",
    "    pca_test_vectors = fft_pca.transform(fft_test_vectors)\n",
    "    param_grid = {'C': [10, 1e2, 1e3, 5e3, 1e4, 5e4, 1e5],\n",
    "                  'gamma': [0.00001, 0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }\n",
    "    clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)\n",
    "    clf = clf.fit(pca_train_vectors, fft_train_labels)\n",
    "    pickle.dump(clf, open(\"hyphal_image_classifier.obj\", \"wb\"))\n",
    "    end = time.time()\n",
    "    print(\"Runtime:\", end-start)\n",
    "    pred_labels = clf.predict(pca_test_vectors)\n",
    "    print(f1_score(fft_test_labels, pred_labels, average='weighted'))\n",
    "    print(n_components, classification_report(fft_test_labels, pred_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_scores = []\n",
    "for i in range(5):\n",
    "    print(\"Running trial SVM\", i+1)\n",
    "    fft_train_vectors, fft_test_vectors, fft_train_labels, fft_test_labels = train_test_split(fft_feature_vector, labels, test_size=0.50)\n",
    "    start = time.time()\n",
    "    param_grid = {'C': [10, 1e2, 1e3, 5e3, 1e4, 5e4, 1e5],\n",
    "                  'gamma': [0.00001, 0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }\n",
    "    clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)\n",
    "    clf = clf.fit(fft_train_vectors, fft_train_labels)\n",
    "    pickle.dump(clf, open(\"hyphal_image_classifier.obj\", \"wb\"))\n",
    "    end = time.time()\n",
    "    print(\"Runtime:\", end-start)\n",
    "    pred_labels = clf.predict(fft_test_vectors)\n",
    "    print(f1_score(fft_test_labels, pred_labels, average='weighted'))\n",
    "    print(n_components, classification_report(fft_test_labels, pred_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fft_feature_vector = get_fft_feat_vector(photo_filenames, './Fungi Pics/', fft=True)\n",
    "best_scores = []\n",
    "for i in range(5):\n",
    "    print(\"Running trial SVM+FFT+PCA\", i+1)\n",
    "    fft_train_vectors, fft_test_vectors, fft_train_labels, fft_test_labels = train_test_split(fft_feature_vector, labels, test_size=0.50)\n",
    "    start = time.time()\n",
    "    n_components = 20\n",
    "    fft_pca = PCA(n_components=n_components, whiten=True)\n",
    "    _ = fft_pca.fit(fft_train_vectors)\n",
    "    pca_train_vectors = fft_pca.transform(fft_train_vectors)\n",
    "    pca_test_vectors = fft_pca.transform(fft_test_vectors)\n",
    "    param_grid = {'C': [10, 1e2, 1e3, 5e3, 1e4, 5e4, 1e5],\n",
    "                  'gamma': [0.00001, 0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }\n",
    "    clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)\n",
    "    clf = clf.fit(pca_train_vectors, fft_train_labels)\n",
    "    pickle.dump(clf, open(\"hyphal_image_classifier.obj\", \"wb\"))\n",
    "    end = time.time()\n",
    "    print(\"Runtime:\", end-start)\n",
    "    pred_labels = clf.predict(pca_test_vectors)\n",
    "    print(f1_score(fft_test_labels, pred_labels, average='weighted'))\n",
    "    print(n_components, classification_report(fft_test_labels, pred_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_scores = []\n",
    "for i in range(5):\n",
    "    print(\"Running trial SVM+FFT\", i+1)\n",
    "    fft_train_vectors, fft_test_vectors, fft_train_labels, fft_test_labels = train_test_split(fft_feature_vector, labels, test_size=0.50)\n",
    "    start = time.time()\n",
    "    param_grid = {'C': [10, 1e2, 1e3, 5e3, 1e4, 5e4, 1e5],\n",
    "                  'gamma': [0.00001, 0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }\n",
    "    clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)\n",
    "    clf = clf.fit(fft_train_vectors, fft_train_labels)\n",
    "    pickle.dump(clf, open(\"hyphal_image_classifier.obj\", \"wb\"))\n",
    "    end = time.time()\n",
    "    print(\"Runtime:\", end-start)\n",
    "    pred_labels = clf.predict(fft_test_vectors)\n",
    "    print(f1_score(fft_test_labels, pred_labels, average='weighted'))\n",
    "    print(n_components, classification_report(fft_test_labels, pred_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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