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Merge pull request #4 from ravi-kumar-pilla/release/v1.0.0
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test release3 aa94739
@github-actions
github-actions[bot] committed Mar 9, 2024
0 parents commit 076cc92
Showing 46 changed files with 2,584 additions and 0 deletions.
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2 changes: 2 additions & 0 deletions 14bc9bf54256a8b7f8a7.worker.js

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9 changes: 9 additions & 0 deletions 14bc9bf54256a8b7f8a7.worker.js.LICENSE.txt
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/** @license kiwi.js v1.1.2
* #------------------------------------------------------------------------------
* # Copyright (c) 2013, Nucleic Development Team & H. Rutjes.
* #
* # Distributed under the terms of the Modified BSD License.
* #
* # The full license is in the file COPYING.txt, distributed with this software.
* #------------------------------------------------------------------------------
**/
1 change: 1 addition & 0 deletions api/deploy-viz-metadata
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{"timestamp": "09.03.2024 01:11:29", "version": "8.0.1"}
617 changes: 617 additions & 0 deletions api/main

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8 changes: 8 additions & 0 deletions api/nodes/23c94afb
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{
"type": "pandas.parquet_dataset.ParquetDataset",
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14 changes: 14 additions & 0 deletions api/nodes/28754fab
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{
"code": "def create_model_input_table(\n shuttles: pd.DataFrame, companies: pd.DataFrame, reviews: pd.DataFrame\n) -> pd.DataFrame:\n \"\"\"Combines all data to create a model input table.\n\n Args:\n shuttles: Preprocessed data for shuttles.\n companies: Preprocessed data for companies.\n reviews: Raw data for reviews.\n Returns:\n Model input table.\n\n \"\"\"\n rated_shuttles = shuttles.merge(reviews, left_on=\"id\", right_on=\"shuttle_id\")\n rated_shuttles = rated_shuttles.drop(\"id\", axis=1)\n model_input_table = rated_shuttles.merge(\n companies, left_on=\"company_id\", right_on=\"id\"\n )\n model_input_table = model_input_table.dropna()\n return model_input_table\n",
"filepath": "shareableviz-action/src/spaceflights_pandas_viz/pipelines/data_processing/nodes.py",
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"outputs": [
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}
13 changes: 13 additions & 0 deletions api/nodes/2ab3579f
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{
"code": "def train_model(X_train: pd.DataFrame, y_train: pd.Series) -> LinearRegression:\n \"\"\"Trains the linear regression model.\n\n Args:\n X_train: Training data of independent features.\n y_train: Training data for price.\n\n Returns:\n Trained model.\n \"\"\"\n regressor = LinearRegression()\n regressor.fit(X_train, y_train)\n return regressor\n",
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]
}
31 changes: 31 additions & 0 deletions api/nodes/329e963c
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{
"code": "def split_data(data: pd.DataFrame, parameters: Dict) -> Tuple:\n \"\"\"Splits data into features and targets training and test sets.\n\n Args:\n data: Data containing features and target.\n parameters: Parameters defined in parameters/data_science.yml.\n Returns:\n Split data.\n \"\"\"\n X = data[parameters[\"features\"]]\n y = data[\"price\"]\n X_train, X_test, y_train, y_test = train_test_split(\n X, y, test_size=parameters[\"test_size\"], random_state=parameters[\"random_state\"]\n )\n return X_train, X_test, y_train, y_test\n",
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"y_train",
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]
}
8 changes: 8 additions & 0 deletions api/nodes/369acf98
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8 changes: 8 additions & 0 deletions api/nodes/493ad53a
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8 changes: 8 additions & 0 deletions api/nodes/4ca0be7b
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90 changes: 90 additions & 0 deletions api/nodes/7b2c6e04
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12 changes: 12 additions & 0 deletions api/nodes/81b5866f
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{
"code": "def compare_passenger_capacity_go(preprocessed_shuttles: pd.DataFrame):\n\n data_frame = (\n preprocessed_shuttles.groupby([\"shuttle_type\"])\n .mean(numeric_only=True)\n .reset_index()\n )\n fig = go.Figure(\n [\n go.Bar(\n x=data_frame[\"shuttle_type\"],\n y=data_frame[\"passenger_capacity\"],\n )\n ]\n )\n\n return fig\n",
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8 changes: 8 additions & 0 deletions api/nodes/872981f9
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8 changes: 8 additions & 0 deletions api/nodes/93d834e1
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8 changes: 8 additions & 0 deletions api/nodes/966b9734
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12 changes: 12 additions & 0 deletions api/nodes/97202ac0
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{
"code": "def compare_passenger_capacity_exp(preprocessed_shuttles: pd.DataFrame):\n return (\n preprocessed_shuttles.groupby([\"shuttle_type\"])\n .mean(numeric_only=True)\n .reset_index()\n )\n",
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8 changes: 8 additions & 0 deletions api/nodes/9ca016a8
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13 changes: 13 additions & 0 deletions api/nodes/9e16ddd2
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{
"code": "def preprocess_companies(companies: pd.DataFrame) -> Tuple[pd.DataFrame, Dict]:\n \"\"\"Preprocesses the data for companies.\n\n Args:\n companies: Raw data.\n Returns:\n Preprocessed data, with `company_rating` converted to a float and\n `iata_approved` converted to boolean.\n \"\"\"\n companies[\"iata_approved\"] = _is_true(companies[\"iata_approved\"])\n companies[\"company_rating\"] = _parse_percentage(companies[\"company_rating\"])\n return companies, {\"columns\": companies.columns.tolist(), \"data_type\": \"companies\"}\n",
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}
8 changes: 8 additions & 0 deletions api/nodes/a9506115
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"type": "plotly.plotly_dataset.PlotlyDataset",
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14 changes: 14 additions & 0 deletions api/nodes/aadc655d
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{
"code": "def evaluate_model(\n regressor: LinearRegression, X_test: pd.DataFrame, y_test: pd.Series\n) -> Dict[str, float]:\n \"\"\"Calculates and logs the coefficient of determination.\n\n Args:\n regressor: Trained model.\n X_test: Testing data of independent features.\n y_test: Testing data for price.\n \"\"\"\n y_pred = regressor.predict(X_test)\n score = r2_score(y_test, y_pred)\n mae = mean_absolute_error(y_test, y_pred)\n me = max_error(y_test, y_pred)\n logger = logging.getLogger(__name__)\n logger.info(\"Model has a coefficient R^2 of %.3f on test data.\", score)\n return {\"r2_score\": score, \"mae\": mae, \"max_error\": me}\n",
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18 changes: 18 additions & 0 deletions api/nodes/ab512939
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{
"parameters": {
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12 changes: 12 additions & 0 deletions api/nodes/acdb05cf
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{
"code": "def preprocess_shuttles(shuttles: pd.DataFrame) -> pd.DataFrame:\n \"\"\"Preprocesses the data for shuttles.\n\n Args:\n shuttles: Raw data.\n Returns:\n Preprocessed data, with `price` converted to a float and `d_check_complete`,\n `moon_clearance_complete` converted to boolean.\n \"\"\"\n shuttles[\"d_check_complete\"] = _is_true(shuttles[\"d_check_complete\"])\n shuttles[\"moon_clearance_complete\"] = _is_true(shuttles[\"moon_clearance_complete\"])\n shuttles[\"price\"] = _parse_money(shuttles[\"price\"])\n return shuttles\n",
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}
60 changes: 60 additions & 0 deletions api/nodes/aed46479
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8 changes: 8 additions & 0 deletions api/nodes/c1311718
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8 changes: 8 additions & 0 deletions api/nodes/cae2d1c7
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12 changes: 12 additions & 0 deletions api/nodes/d75ec4b0
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"code": "def create_confusion_matrix(companies: pd.DataFrame):\n actuals = [0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1]\n predicted = [1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1]\n data = {\"y_Actual\": actuals, \"y_Predicted\": predicted}\n df = pd.DataFrame(data, columns=[\"y_Actual\", \"y_Predicted\"])\n confusion_matrix = pd.crosstab(\n df[\"y_Actual\"], df[\"y_Predicted\"], rownames=[\"Actual\"], colnames=[\"Predicted\"]\n )\n sn.heatmap(confusion_matrix, annot=True)\n return plt\n",
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617 changes: 617 additions & 0 deletions api/pipelines/__default__

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250 changes: 250 additions & 0 deletions api/pipelines/data_processing
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