Cleaned up path handling in chapter 09

Author: wrichert

ch09/01_fft_based_classifier.py

@@ -14,12 +14,10 @@
 
 from sklearn.metrics import confusion_matrix
 
-from utils import plot_pr, plot_roc, plot_confusion_matrix, GENRE_LIST
+from utils import plot_pr, plot_roc, plot_confusion_matrix, GENRE_LIST, TEST_DIR
 
 from fft import read_fft
 
-TEST_DIR = "/media/sf_P/pymlbook-data/09-genre-class/private"
-
 genre_list = GENRE_LIST
 
 
@@ -83,7 +81,7 @@ def train_model(clf_factory, X, Y, name, plot=False):
 
     if plot:
         for label in labels:
-            print "Plotting", genre_list[label]
+            print("Plotting %s"%genre_list[label])
             scores_to_sort = roc_scores[label]
             median = np.argsort(scores_to_sort)[len(scores_to_sort) / 2]
 
@@ -96,7 +94,7 @@ def train_model(clf_factory, X, Y, name, plot=False):
     all_pr_scores = np.asarray(pr_scores.values()).flatten()
     summary = (np.mean(scores), np.std(scores),
                np.mean(all_pr_scores), np.std(all_pr_scores))
-    print "%.3f\t%.3f\t%.3f\t%.3f\t" % summary
+    print("%.3f\t%.3f\t%.3f\t%.3f\t" % summary)
 
     return np.mean(train_errors), np.mean(test_errors), np.asarray(cms)
 
@@ -117,7 +115,5 @@ def create_model():
     cm_avg = np.mean(cms, axis=0)
     cm_norm = cm_avg / np.sum(cm_avg, axis=0)
 
-    print cm_norm
-
     plot_confusion_matrix(cm_norm, genre_list, "fft",
                           "Confusion matrix of an FFT based classifier")

ch09/02_ceps_based_classifier.py

@@ -14,11 +14,10 @@
 
 from sklearn.metrics import confusion_matrix
 
-from utils import plot_roc, plot_confusion_matrix, GENRE_LIST
+from utils import plot_roc, plot_confusion_matrix, GENRE_LIST, TEST_DIR
 
 from ceps import read_ceps
 
-TEST_DIR = "/media/sf_P/pymlbook-data/09-genre-class/private"
 
 genre_list = GENRE_LIST
 
@@ -83,7 +82,7 @@ def train_model(clf_factory, X, Y, name, plot=False):
 
     if plot:
         for label in labels:
-            print "Plotting", genre_list[label]
+            print("Plotting %s"%genre_list[label])
             scores_to_sort = roc_scores[label]
             median = np.argsort(scores_to_sort)[len(scores_to_sort) / 2]
 
@@ -94,7 +93,7 @@ def train_model(clf_factory, X, Y, name, plot=False):
     all_pr_scores = np.asarray(pr_scores.values()).flatten()
     summary = (np.mean(scores), np.std(scores),
                np.mean(all_pr_scores), np.std(all_pr_scores))
-    print "%.3f\t%.3f\t%.3f\t%.3f\t" % summary
+    print("%.3f\t%.3f\t%.3f\t%.3f\t" % summary)
 
     return np.mean(train_errors), np.mean(test_errors), np.asarray(cms)
 
@@ -115,7 +114,5 @@ def create_model():
     cm_avg = np.mean(cms, axis=0)
     cm_norm = cm_avg / np.sum(cm_avg, axis=0)
 
-    print cm_norm
-
     plot_confusion_matrix(cm_norm, genre_list, "ceps",
                           "Confusion matrix of a CEPS based classifier")

ch09/Makefile

@@ -1,17 +1,4 @@
-CHART_DIR = ../charts
-TARGET_DIR = /media/sf_P/Dropbox/pymlbook/pmle/ch09
-
-copy: rocs_fft.png rocs_ceps.png fft_demo 
-	#cp $(CHART_DIR)/Spectrogram_Genres.png $(TARGET_DIR)/1400_09_01.png
-	cp $(CHART_DIR)/confusion_matrix_fft.png $(TARGET_DIR)/1400_09_02.png
-	cp $(CHART_DIR)/rocs_fft.png $(TARGET_DIR)/1400_09_03.png
-	cp $(CHART_DIR)/rocs_ceps.png $(TARGET_DIR)/1400_09_04.png
-	cp fft_demo.png $(TARGET_DIR)/1400_09_05.png
-	cp fft_example.png $(TARGET_DIR)/1400_09_06.png
-	cp $(CHART_DIR)/confusion_matrix_ceps.png $(TARGET_DIR)/1400_09_07.png
-	cp roc_pr.png $(TARGET_DIR)/1400_09_08.png
-	cp *.py $(TARGET_DIR)/code
-	cp Makefile $(TARGET_DIR)/code
+CHART_DIR = charts
 
 fft:
 	python 01_fft_based_classifier.py

ch09/utils.py

@@ -6,16 +6,33 @@
 # It is made available under the MIT License
 
 import os
+import sys
 
 from matplotlib import pylab
 import numpy as np
 
-DATA_DIR = os.path.join("..", "data")
-CHART_DIR = os.path.join("..", "charts")
+DATA_DIR = os.path.join(
+    os.path.dirname(os.path.realpath(__file__)), "data")
 
-GENRE_DIR = "/media/sf_P/pymlbook-data/09-genre-class/genres"
+CHART_DIR = os.path.join(
+    os.path.dirname(os.path.realpath(__file__)), "charts")
+
+for d in [DATA_DIR, CHART_DIR]:
+    if not os.path.exists(d):
+        os.mkdir(d)
+
+# Put your directory to the different music genres here
+GENRE_DIR = None
 GENRE_LIST = ["classical", "jazz", "country", "pop", "rock", "metal"]
 
+# Put your directory to the test dir here
+TEST_DIR = None
+
+if GENRE_DIR is None or TEST_DIR is None:
+    print("Please set GENRE_DIR and TEST_DIR in utils.py") 
+    sys.exit(1)
+
+
 
 def plot_confusion_matrix(cm, genre_list, name, title):
     pylab.clf()