Ipython embed script

pylearn2/scripts/mlp/predict_csv.py

@@ -19,7 +19,8 @@
 from theano import tensor as T
 from theano import function
 
-if __name__ == "__main__":
+
+def main():
     try:
         model_path = sys.argv[1]
         test_path = sys.argv[2]
@@ -28,7 +29,10 @@
         print "Usage: predict.py <model file> <test file> <output file>"
         print "       predict.py saved_model.pkl test_x.csv predictions.csv\n"
         quit(-1)
+    run(model_path, test_path, out_path)
+
 
+def run(model_path, test_path, out_path, headers=False, cast_to_float32=False, first_col_label=False):
     print "loading model..."
 
     try:
@@ -52,7 +56,14 @@
 
 # x is a numpy array
 # x = pickle.load(open(test_path, 'rb'))
-    x = np.loadtxt(test_path, delimiter=',')	# no labels in the file
+    # only skip first row if labels in the file
+    skiprows = 1 if headers else 0
+    x = np.loadtxt(test_path, delimiter=',', skiprows=skiprows)
+    if cast_to_float32:
+        x = x.astype(np.float32)
+
+    if first_col_label:
+        x = x[:,1:]
 
     y = f(x)
 
@@ -61,3 +72,6 @@
     np.savetxt(out_path, y, fmt='%d')
 
 
+if __name__ == "__main__":
+    main()
+

pylearn2/scripts/plot_monitor.py

@@ -68,8 +68,12 @@ def main():
     parser.add_argument("model_paths", nargs='+')
     options = parser.parse_args()
     model_paths = options.model_paths
+    options_out = options.out
 
-    if options.out is not None:
+    plot_monitor(model_paths, options_out)
+
+def plot_monitor(model_paths=[], options_out=None, show_codes=None, x_axis='example'):
+    if options_out is not None:
       import matplotlib
       matplotlib.use('Agg')
     import matplotlib.pyplot as plt
@@ -93,7 +97,7 @@ def main():
             raise
         this_model_channels = model.monitor.channels
 
-        if len(sys.argv) > 2:
+        if len(model_paths) > 1:
             postfix = ":" + model_names[i]
         else:
             postfix = ""
@@ -117,18 +121,16 @@ def main():
             codebook['<'+channel_name+'>'] = channel_name
             sorted_codes.append(code)
 
-        x_axis = 'example'
-        print 'set x_axis to example'
-
         if len(channels.values()) == 0:
             print "there are no channels to plot"
             break
 
         # If there is more than one channel in the monitor ask which ones to
         # plot
         prompt = len(channels.values()) > 1
-
-        if prompt:
+        if show_codes:
+            final_codes = show_codes
+        elif prompt:
 
             # Display the codebook
             for code in sorted_codes:
@@ -221,6 +223,8 @@ def main():
         else:
             final_codes ,= set(codebook.keys())
 
+        print 'set x_axis to %s' % x_axis
+
         colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
         styles = list(colors)
         styles += [color+'--' for color in colors]
@@ -232,7 +236,10 @@ def main():
         # plot the requested channels
         for idx, code in enumerate(sorted(final_codes)):
 
-            channel_name= codebook[code]
+            if code in codebook.values():
+                channel_name = code
+            else:
+                channel_name = codebook[code]
             channel = channels[channel_name]
 
             y = np.asarray(channel.val_record)
@@ -276,12 +283,12 @@ def main():
         # 0.046 is the size of 1 legend box
         fig.subplots_adjust(bottom=0.11 + 0.046 * len(final_codes))
 
-        if options.out is None:
+        if options_out is None:
           plt.show()
         else:
-          plt.savefig(options.out)
+          plt.savefig(options_out)
 
-        if not prompt:
+        if not prompt or show_codes:
             break
 
 if __name__ == "__main__":

pylearn2/utils/image.py

@@ -112,6 +112,11 @@ def show(image):
         If ndarray, integer formats are assumed to use 0-255
         and float formats are assumed to use 0-1
     """
+    viewer_command = string.preprocess('${PYLEARN2_VIEWER_COMMAND}')
+
+    if viewer_command == 'inline':
+        return imview(image)
+
     if hasattr(image, '__array__'):
         #do some shape checking because PIL just raises a tuple indexing error
         #that doesn't make it very clear what the problem is
@@ -160,7 +165,6 @@ def show(image):
     #
 
     image.save(name)
-    viewer_command = string.preprocess('${PYLEARN2_VIEWER_COMMAND}')
     if os.name == 'nt':
         subprocess.Popen(viewer_command + ' ' + name +' && del ' + name,
                          shell=True)