project 1 updated

python/Supervised Learning/Project/visuals.py

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+###########################################
+# Suppress matplotlib user warnings
+# Necessary for newer version of matplotlib
+import warnings
+warnings.filterwarnings("ignore", category = UserWarning, module = "matplotlib")
+#
+# Display inline matplotlib plots with IPython
+from IPython import get_ipython
+get_ipython().run_line_magic('matplotlib', 'inline')
+###########################################
+
+import matplotlib.pyplot as pl
+import matplotlib.patches as mpatches
+import numpy as np
+import pandas as pd
+from time import time
+from sklearn.metrics import f1_score, accuracy_score
+
+
+def distribution(data, transformed = False):
+    """
+    Visualization code for displaying skewed distributions of features
+    """
+    
+    # Create figure
+    fig = pl.figure(figsize = (11,5));
+
+    # Skewed feature plotting
+    for i, feature in enumerate(['capital-gain','capital-loss']):
+        ax = fig.add_subplot(1, 2, i+1)
+        ax.hist(data[feature], bins = 25, color = '#00A0A0')
+        ax.set_title("'%s' Feature Distribution"%(feature), fontsize = 14)
+        ax.set_xlabel("Value")
+        ax.set_ylabel("Number of Records")
+        ax.set_ylim((0, 2000))
+        ax.set_yticks([0, 500, 1000, 1500, 2000])
+        ax.set_yticklabels([0, 500, 1000, 1500, ">2000"])
+
+    # Plot aesthetics
+    if transformed:
+        fig.suptitle("Log-transformed Distributions of Continuous Census Data Features", \
+            fontsize = 16, y = 1.03)
+    else:
+        fig.suptitle("Skewed Distributions of Continuous Census Data Features", \
+            fontsize = 16, y = 1.03)
+
+    fig.tight_layout()
+    fig.show()
+
+
+def evaluate(results, accuracy, f1):
+    """
+    Visualization code to display results of various learners.
+    
+    inputs:
+      - learners: a list of supervised learners
+      - stats: a list of dictionaries of the statistic results from 'train_predict()'
+      - accuracy: The score for the naive predictor
+      - f1: The score for the naive predictor
+    """
+  
+    # Create figure
+    fig, ax = pl.subplots(2, 3, figsize = (11,7))
+
+    # Constants
+    bar_width = 0.3
+    colors = ['#A00000','#00A0A0','#00A000']
+    
+    # Super loop to plot four panels of data
+    for k, learner in enumerate(results.keys()):
+        for j, metric in enumerate(['train_time', 'acc_train', 'f_train', 'pred_time', 'acc_test', 'f_test']):
+            for i in np.arange(3):
+                
+                # Creative plot code
+                ax[j//3, j%3].bar(i+k*bar_width, results[learner][i][metric], width = bar_width, color = colors[k])
+                ax[j//3, j%3].set_xticks([0.45, 1.45, 2.45])
+                ax[j//3, j%3].set_xticklabels(["1%", "10%", "100%"])
+                ax[j//3, j%3].set_xlabel("Training Set Size")
+                ax[j//3, j%3].set_xlim((-0.1, 3.0))
+    
+    # Add unique y-labels
+    ax[0, 0].set_ylabel("Time (in seconds)")
+    ax[0, 1].set_ylabel("Accuracy Score")
+    ax[0, 2].set_ylabel("F-score")
+    ax[1, 0].set_ylabel("Time (in seconds)")
+    ax[1, 1].set_ylabel("Accuracy Score")
+    ax[1, 2].set_ylabel("F-score")
+    
+    # Add titles
+    ax[0, 0].set_title("Model Training")
+    ax[0, 1].set_title("Accuracy Score on Training Subset")
+    ax[0, 2].set_title("F-score on Training Subset")
+    ax[1, 0].set_title("Model Predicting")
+    ax[1, 1].set_title("Accuracy Score on Testing Set")
+    ax[1, 2].set_title("F-score on Testing Set")
+    
+    # Add horizontal lines for naive predictors
+    ax[0, 1].axhline(y = accuracy, xmin = -0.1, xmax = 3.0, linewidth = 1, color = 'k', linestyle = 'dashed')
+    ax[1, 1].axhline(y = accuracy, xmin = -0.1, xmax = 3.0, linewidth = 1, color = 'k', linestyle = 'dashed')
+    ax[0, 2].axhline(y = f1, xmin = -0.1, xmax = 3.0, linewidth = 1, color = 'k', linestyle = 'dashed')
+    ax[1, 2].axhline(y = f1, xmin = -0.1, xmax = 3.0, linewidth = 1, color = 'k', linestyle = 'dashed')
+    
+    # Set y-limits for score panels
+    ax[0, 1].set_ylim((0, 1))
+    ax[0, 2].set_ylim((0, 1))
+    ax[1, 1].set_ylim((0, 1))
+    ax[1, 2].set_ylim((0, 1))
+
+    # Create patches for the legend
+    patches = []
+    for i, learner in enumerate(results.keys()):
+        patches.append(mpatches.Patch(color = colors[i], label = learner))
+    pl.legend(handles = patches, bbox_to_anchor = (-.80, 2.53), \
+               loc = 'upper center', borderaxespad = 0., ncol = 3, fontsize = 'x-large')
+    
+    # Aesthetics
+    pl.suptitle("Performance Metrics for Three Supervised Learning Models", fontsize = 16, y = 1.10)
+    pl.tight_layout()
+    pl.show()
+    
+
+def feature_plot(importances, X_train, y_train):
+    
+    # Display the five most important features
+    indices = np.argsort(importances)[::-1]
+    columns = X_train.columns.values[indices[:5]]
+    values = importances[indices][:5]
+
+    # Creat the plot
+    fig = pl.figure(figsize = (9,5))
+    pl.title("Normalized Weights for First Five Most Predictive Features", fontsize = 16)
+    pl.bar(np.arange(5), values, width = 0.6, align="center", color = '#00A000', \
+          label = "Feature Weight")
+    pl.bar(np.arange(5) - 0.3, np.cumsum(values), width = 0.2, align = "center", color = '#00A0A0', \
+          label = "Cumulative Feature Weight")
+    pl.xticks(np.arange(5), columns)
+    pl.xlim((-0.5, 4.5))
+    pl.ylabel("Weight", fontsize = 12)
+    pl.xlabel("Feature", fontsize = 12)
+    
+    pl.legend(loc = 'upper center')
+    pl.tight_layout()
+    pl.show()