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amaiya · Mar 22, 2023 · e8bd20c · e8bd20c
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Showing 7 changed files with 199 additions and 150 deletions.
diff --git a/docs/graph/predictor.html b/docs/graph/predictor.html
@@ -52,10 +52,12 @@ <h1 class="title">Module <code>ktrain.graph.predictor</code></h1>
     def get_classes(self):
         return self.c
 
-    def predict(self, node_ids, return_proba=False):
-        return self.predict_transductive(node_ids, return_proba=return_proba)
+    def predict(self, node_ids, return_proba=False, verbose=0):
+        return self.predict_transductive(
+            node_ids, return_proba=return_proba, verbose=verbose
+        )
 
-    def predict_transductive(self, node_ids, return_proba=False):
+    def predict_transductive(self, node_ids, return_proba=False, verbose=0):
         &#34;&#34;&#34;
         ```
         Performs transductive inference.
@@ -66,11 +68,11 @@ <h1 class="title">Module <code>ktrain.graph.predictor</code></h1>
         gen.batch_size = self.batch_size
         # *_generator methods are deprecated from TF 2.1.0
         # preds = self.model.predict_generator(gen)
-        preds = self.model.predict(gen)
+        preds = self.model.predict(gen, verbose=verbose)
         result = preds if return_proba else [self.c[np.argmax(pred)] for pred in preds]
         return result
 
-    def predict_inductive(self, df, G, return_proba=False):
+    def predict_inductive(self, df, G, return_proba=False, verbose=0):
         &#34;&#34;&#34;
         ```
         Performs inductive inference.
@@ -82,7 +84,7 @@ <h1 class="title">Module <code>ktrain.graph.predictor</code></h1>
         gen.batch_size = self.batch_size
         # *_generator methods are deprecated from TF 2.1.0
         # preds = self.model.predict_generator(gen)
-        preds = self.model.predict(gen)
+        preds = self.model.predict(gen, verbose=verbose)
         result = preds if return_proba else [self.c[np.argmax(pred)] for pred in preds]
         return result
 
@@ -107,7 +109,7 @@ <h1 class="title">Module <code>ktrain.graph.predictor</code></h1>
     def get_classes(self):
         return self.c
 
-    def predict(self, G, edge_ids, return_proba=False):
+    def predict(self, G, edge_ids, return_proba=False, verbose=0):
         &#34;&#34;&#34;
         ```
         Performs link prediction
@@ -118,7 +120,7 @@ <h1 class="title">Module <code>ktrain.graph.predictor</code></h1>
         gen.batch_size = self.batch_size
         # *_generator methods are deprecated from TF 2.1.0
         # preds = self.model.predict_generator(gen)
-        preds = self.model.predict(gen)
+        preds = self.model.predict(gen, verbose=verbose)
         preds = np.squeeze(preds)
         if return_proba:
             return [[1 - pred, pred] for pred in preds]
@@ -166,7 +168,7 @@ <h2 class="section-title" id="header-classes">Classes</h2>
     def get_classes(self):
         return self.c
 
-    def predict(self, G, edge_ids, return_proba=False):
+    def predict(self, G, edge_ids, return_proba=False, verbose=0):
         &#34;&#34;&#34;
         ```
         Performs link prediction
@@ -177,7 +179,7 @@ <h2 class="section-title" id="header-classes">Classes</h2>
         gen.batch_size = self.batch_size
         # *_generator methods are deprecated from TF 2.1.0
         # preds = self.model.predict_generator(gen)
-        preds = self.model.predict(gen)
+        preds = self.model.predict(gen, verbose=verbose)
         preds = np.squeeze(preds)
         if return_proba:
             return [[1 - pred, pred] for pred in preds]
@@ -205,7 +207,7 @@ <h3>Methods</h3>
 </details>
 </dd>
 <dt id="ktrain.graph.predictor.LinkPredictor.predict"><code class="name flex">
-<span>def <span class="ident">predict</span></span>(<span>self, G, edge_ids, return_proba=False)</span>
+<span>def <span class="ident">predict</span></span>(<span>self, G, edge_ids, return_proba=False, verbose=0)</span>
 </code></dt>
 <dd>
 <div class="desc"><pre><code>Performs link prediction
@@ -215,7 +217,7 @@ <h3>Methods</h3>
 <summary>
 <span>Expand source code</span>
 </summary>
-<pre><code class="python">def predict(self, G, edge_ids, return_proba=False):
+<pre><code class="python">def predict(self, G, edge_ids, return_proba=False, verbose=0):
     &#34;&#34;&#34;
     ```
     Performs link prediction
@@ -226,7 +228,7 @@ <h3>Methods</h3>
     gen.batch_size = self.batch_size
     # *_generator methods are deprecated from TF 2.1.0
     # preds = self.model.predict_generator(gen)
-    preds = self.model.predict(gen)
+    preds = self.model.predict(gen, verbose=verbose)
     preds = np.squeeze(preds)
     if return_proba:
         return [[1 - pred, pred] for pred in preds]
@@ -278,10 +280,12 @@ <h3>Inherited members</h3>
     def get_classes(self):
         return self.c
 
-    def predict(self, node_ids, return_proba=False):
-        return self.predict_transductive(node_ids, return_proba=return_proba)
+    def predict(self, node_ids, return_proba=False, verbose=0):
+        return self.predict_transductive(
+            node_ids, return_proba=return_proba, verbose=verbose
+        )
 
-    def predict_transductive(self, node_ids, return_proba=False):
+    def predict_transductive(self, node_ids, return_proba=False, verbose=0):
         &#34;&#34;&#34;
         ```
         Performs transductive inference.
@@ -292,11 +296,11 @@ <h3>Inherited members</h3>
         gen.batch_size = self.batch_size
         # *_generator methods are deprecated from TF 2.1.0
         # preds = self.model.predict_generator(gen)
-        preds = self.model.predict(gen)
+        preds = self.model.predict(gen, verbose=verbose)
         result = preds if return_proba else [self.c[np.argmax(pred)] for pred in preds]
         return result
 
-    def predict_inductive(self, df, G, return_proba=False):
+    def predict_inductive(self, df, G, return_proba=False, verbose=0):
         &#34;&#34;&#34;
         ```
         Performs inductive inference.
@@ -308,7 +312,7 @@ <h3>Inherited members</h3>
         gen.batch_size = self.batch_size
         # *_generator methods are deprecated from TF 2.1.0
         # preds = self.model.predict_generator(gen)
-        preds = self.model.predict(gen)
+        preds = self.model.predict(gen, verbose=verbose)
         result = preds if return_proba else [self.c[np.argmax(pred)] for pred in preds]
         return result</code></pre>
 </details>
@@ -333,20 +337,22 @@ <h3>Methods</h3>
 </details>
 </dd>
 <dt id="ktrain.graph.predictor.NodePredictor.predict"><code class="name flex">
-<span>def <span class="ident">predict</span></span>(<span>self, node_ids, return_proba=False)</span>
+<span>def <span class="ident">predict</span></span>(<span>self, node_ids, return_proba=False, verbose=0)</span>
 </code></dt>
 <dd>
 <div class="desc"></div>
 <details class="source">
 <summary>
 <span>Expand source code</span>
 </summary>
-<pre><code class="python">def predict(self, node_ids, return_proba=False):
-    return self.predict_transductive(node_ids, return_proba=return_proba)</code></pre>
+<pre><code class="python">def predict(self, node_ids, return_proba=False, verbose=0):
+    return self.predict_transductive(
+        node_ids, return_proba=return_proba, verbose=verbose
+    )</code></pre>
 </details>
 </dd>
 <dt id="ktrain.graph.predictor.NodePredictor.predict_inductive"><code class="name flex">
-<span>def <span class="ident">predict_inductive</span></span>(<span>self, df, G, return_proba=False)</span>
+<span>def <span class="ident">predict_inductive</span></span>(<span>self, df, G, return_proba=False, verbose=0)</span>
 </code></dt>
 <dd>
 <div class="desc"><pre><code>Performs inductive inference.
@@ -356,7 +362,7 @@ <h3>Methods</h3>
 <summary>
 <span>Expand source code</span>
 </summary>
-<pre><code class="python">def predict_inductive(self, df, G, return_proba=False):
+<pre><code class="python">def predict_inductive(self, df, G, return_proba=False, verbose=0):
     &#34;&#34;&#34;
     ```
     Performs inductive inference.
@@ -368,13 +374,13 @@ <h3>Methods</h3>
     gen.batch_size = self.batch_size
     # *_generator methods are deprecated from TF 2.1.0
     # preds = self.model.predict_generator(gen)
-    preds = self.model.predict(gen)
+    preds = self.model.predict(gen, verbose=verbose)
     result = preds if return_proba else [self.c[np.argmax(pred)] for pred in preds]
     return result</code></pre>
 </details>
 </dd>
 <dt id="ktrain.graph.predictor.NodePredictor.predict_transductive"><code class="name flex">
-<span>def <span class="ident">predict_transductive</span></span>(<span>self, node_ids, return_proba=False)</span>
+<span>def <span class="ident">predict_transductive</span></span>(<span>self, node_ids, return_proba=False, verbose=0)</span>
 </code></dt>
 <dd>
 <div class="desc"><pre><code>Performs transductive inference.
@@ -384,7 +390,7 @@ <h3>Methods</h3>
 <summary>
 <span>Expand source code</span>
 </summary>
-<pre><code class="python">def predict_transductive(self, node_ids, return_proba=False):
+<pre><code class="python">def predict_transductive(self, node_ids, return_proba=False, verbose=0):
     &#34;&#34;&#34;
     ```
     Performs transductive inference.
@@ -395,7 +401,7 @@ <h3>Methods</h3>
     gen.batch_size = self.batch_size
     # *_generator methods are deprecated from TF 2.1.0
     # preds = self.model.predict_generator(gen)
-    preds = self.model.predict(gen)
+    preds = self.model.predict(gen, verbose=verbose)
     result = preds if return_proba else [self.c[np.argmax(pred)] for pred in preds]
     return result</code></pre>
 </details>

diff --git a/docs/tabular/predictor.html b/docs/tabular/predictor.html
@@ -55,13 +55,14 @@ <h1 class="title">Module <code>ktrain.tabular.predictor</code></h1>
     def get_classes(self):
         return self.c
 
-    def predict(self, df, return_proba=False):
+    def predict(self, df, return_proba=False, verbose=0):
         &#34;&#34;&#34;
         ```
         Makes predictions for a test dataframe
         Args:
           df(pd.DataFrame):  a pandas DataFrame in same format as DataFrame used for training model
           return_proba(bool): If True, return probabilities instead of predicted class labels
+          verbose(int): verbosity: 0 (silent), 1 (progress bar), 2 (single line)
         ```
         &#34;&#34;&#34;
         if not isinstance(df, pd.DataFrame):
@@ -73,7 +74,7 @@ <h1 class="title">Module <code>ktrain.tabular.predictor</code></h1>
         # get predictions
         tseq = self.preproc.preprocess_test(df, verbose=0)
         tseq.batch_size = self.batch_size
-        preds = self.model.predict(tseq)
+        preds = self.model.predict(tseq, verbose=verbose)
         result = (
             preds
             if return_proba or multilabel or not self.c
@@ -248,13 +249,14 @@ <h2 class="section-title" id="header-classes">Classes</h2>
     def get_classes(self):
         return self.c
 
-    def predict(self, df, return_proba=False):
+    def predict(self, df, return_proba=False, verbose=0):
         &#34;&#34;&#34;
         ```
         Makes predictions for a test dataframe
         Args:
           df(pd.DataFrame):  a pandas DataFrame in same format as DataFrame used for training model
           return_proba(bool): If True, return probabilities instead of predicted class labels
+          verbose(int): verbosity: 0 (silent), 1 (progress bar), 2 (single line)
         ```
         &#34;&#34;&#34;
         if not isinstance(df, pd.DataFrame):
@@ -266,7 +268,7 @@ <h2 class="section-title" id="header-classes">Classes</h2>
         # get predictions
         tseq = self.preproc.preprocess_test(df, verbose=0)
         tseq.batch_size = self.batch_size
-        preds = self.model.predict(tseq)
+        preds = self.model.predict(tseq, verbose=verbose)
         result = (
             preds
             if return_proba or multilabel or not self.c
@@ -554,25 +556,27 @@ <h3>Methods</h3>
 </details>
 </dd>
 <dt id="ktrain.tabular.predictor.TabularPredictor.predict"><code class="name flex">
-<span>def <span class="ident">predict</span></span>(<span>self, df, return_proba=False)</span>
+<span>def <span class="ident">predict</span></span>(<span>self, df, return_proba=False, verbose=0)</span>
 </code></dt>
 <dd>
 <div class="desc"><pre><code>Makes predictions for a test dataframe
 Args:
   df(pd.DataFrame):  a pandas DataFrame in same format as DataFrame used for training model
   return_proba(bool): If True, return probabilities instead of predicted class labels
+  verbose(int): verbosity: 0 (silent), 1 (progress bar), 2 (single line)
 </code></pre></div>
 <details class="source">
 <summary>
 <span>Expand source code</span>
 </summary>
-<pre><code class="python">def predict(self, df, return_proba=False):
+<pre><code class="python">def predict(self, df, return_proba=False, verbose=0):
     &#34;&#34;&#34;
     ```
     Makes predictions for a test dataframe
     Args:
       df(pd.DataFrame):  a pandas DataFrame in same format as DataFrame used for training model
       return_proba(bool): If True, return probabilities instead of predicted class labels
+      verbose(int): verbosity: 0 (silent), 1 (progress bar), 2 (single line)
     ```
     &#34;&#34;&#34;
     if not isinstance(df, pd.DataFrame):
@@ -584,7 +588,7 @@ <h3>Methods</h3>
     # get predictions
     tseq = self.preproc.preprocess_test(df, verbose=0)
     tseq.batch_size = self.batch_size
-    preds = self.model.predict(tseq)
+    preds = self.model.predict(tseq, verbose=verbose)
     result = (
         preds
         if return_proba or multilabel or not self.c

diff --git a/docs/text/ner/anago/preprocessing.html b/docs/text/ner/anago/preprocessing.html
@@ -790,7 +790,6 @@ <h3>Ancestors</h3>
 <ul class="hlist">
 <li>sklearn.base.BaseEstimator</li>
 <li>sklearn.base.TransformerMixin</li>
-<li>sklearn.utils._set_output._SetOutputMixin</li>
 </ul>
 <h3>Static methods</h3>
 <dl>