added plot_functions with plot_confusion_matrix

src/ml_things/__init__.py

@@ -8,6 +8,7 @@ __version__ = "0.0.1"
 from .array_functions import (pad_array,
                               batch_array)
 from .web_related import (download_from)
+from .plot_functions import (plot_confusion_matrix)
 
 # alternative names
 from .array_functions import batch_array as chunk_array

src/ml_things/plot_functions.py

@@ -0,0 +1,105 @@
+# coding=utf-8
+# Copyright 2020 George Mihaila.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Functions related to plotting"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.metrics import confusion_matrix
+
+
+def plot_confusion_matrix(y_true, y_pred, classes='', normalize=False, title=None, cmap=plt.cm.Blues, image=None,
+                          verbose=0, magnify=1.2, dpi=50):
+    """This function prints and plots the confusion matrix.
+    Normalization can be applied by setting `normalize=True`.
+    y_true needs to contain all possible labels.
+
+    :param y_true: array labels values.
+    :param y_pred: array predicted label values.
+    :param classes: array list of label names.
+    :param normalize: bool normalize confusion matrix or not.
+    :param title: str string title of plot.
+    :param cmap: plt.cm plot theme
+    :param image: str path to save plot in an image.
+    :param verbose: int print confusion matrix when calling function.
+    :param magnify: int zoom of plot.
+    :param dpi: int clarity of plot.
+    :return: array confusion matrix used to plot.
+
+    Note:
+        - Plot themes:
+        cmap=plt.cm.Blues - used as default.
+        cmap=plt.cm.BuPu
+        cmap=plt.cm.GnBu
+        cmap=plt.cm.Greens
+        cmap=plt.cm.OrRd
+    """
+    if len(y_true) != len(y_pred):
+        # make sure lengths match
+        raise ValueError("`y_true` needs to have same length as `y_pred`!")
+
+    # Class labels setup. If none, generate from y_true y_pred
+    classes = list(classes)
+    if classes:
+        assert len(set(y_true)) == len(classes)
+    else:
+        classes = set(y_true)
+    # Title setup
+    if not title:
+        if normalize:
+            title = 'Normalized confusion matrix'
+        else:
+            title = 'Confusion matrix, without normalization'
+    # Compute confusion matrix
+    cm = confusion_matrix(y_true, y_pred)
+    # Normalize setup
+    if normalize:
+        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
+        print("Normalized confusion matrix")
+    else:
+        print('Confusion matrix, without normalization')
+    # Print if verbose
+    if verbose > 0:
+        print(cm)
+    # Plot setup
+    fig, ax = plt.subplots()
+    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
+    ax.figure.colorbar(im, ax=ax)
+    # We want to show all ticks...
+    ax.set(xticks=np.arange(cm.shape[1]),
+           yticks=np.arange(cm.shape[0]),
+           # ... and label them with the respective list entries
+           xticklabels=classes, yticklabels=classes,
+           title=title,
+           ylabel='True label',
+           xlabel='Predicted label')
+    # Rotate the tick labels and set their alignment.
+    plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
+             rotation_mode="anchor")
+    plt.grid(False)
+    # Loop over data dimensions and create text annotations.
+    fmt = '.2f' if normalize else 'd'
+    thresh = cm.max() / 2.
+    for i in range(cm.shape[0]):
+        for j in range(cm.shape[1]):
+            ax.text(j, i, format(cm[i, j], fmt),
+                    ha="center", va="center",
+                    color="white" if cm[i, j] > thresh else "black")
+    fig.tight_layout()
+    fig = plt.gcf()
+    figsize = fig.get_size_inches()
+    fig.set_size_inches(figsize * magnify)
+    if image:
+        fig.savefig(image, dpi=dpi)
+    return cm