import numpy as np import matplotlib.pyplot as plt from matplotlib import patches from sklearn.svm import LinearSVC from matplotlib.axes._axes import _log as matplotlib_axes_logger matplotlib_axes_logger.setLevel('ERROR') colors = ['#e6194B', '#3cb44b', '#ffe119', '#4363d8', '#f58231', '#911eb4', '#42d4f4', '#f032e6', '#bfef45', '#fabebe', '#469990', '#e6beff', '#9A6324', '#fffac8', '#800000', '#aaffc3', '#808000', '#ffd8b1', '#000075', '#a9a9a9'] class InteractivePlotter: def __init__(self, feats_ds, feats, spec_slices, call_info, freq_lims, allow_training): """ Plots 2D low dimensional features on left and corresponding spectgrams on the right. """ self.feats_ds = feats_ds self.feats = feats self.clf = None self.spec_slices = spec_slices self.call_info = call_info #_, self.labels = np.unique([cc['class'] for cc in call_info], return_inverse=True) self.labels = np.zeros(len(call_info), dtype=np.int) self.annotated = np.zeros(self.labels.shape[0], dtype=np.int) # can populate this with 1's where we have labels self.labels_cols = [colors[self.labels[ii]] for ii in range(len(self.labels))] self.freq_lims = freq_lims self.allow_training = allow_training self.pt_size = 5.0 self.spec_pad = 0.2 # this much padding has been applied to the spec slices self.fig_width = 12 self.fig_height = 8 self.current_id = 0 max_ind = np.argmax([ss.shape[1] for ss in self.spec_slices]) self.max_width = self.spec_slices[max_ind].shape[1] self.blank_spec = np.zeros((self.spec_slices[0].shape[0], self.max_width)) def plot(self, fig_id): self.fig, self.ax = plt.subplots(nrows=1, ncols=2, num=fig_id, figsize=(self.fig_width, self.fig_height), gridspec_kw={'width_ratios': [2, 1]}) plt.tight_layout() # plot 2D TNSE features self.low_dim_plt = self.ax[0].scatter(self.feats_ds[:, 0], self.feats_ds[:, 1], c=self.labels_cols, s=self.pt_size, picker=5) self.ax[0].set_title('TSNE of Call Features') self.ax[0].set_xticks([]) self.ax[0].set_yticks([]) # plot clip from spectrogram spec_min_max = (0, self.blank_spec.shape[1], self.freq_lims[0], self.freq_lims[1]) self.ax[1].imshow(self.blank_spec, extent=spec_min_max, cmap='plasma', aspect='auto') self.spec_im = self.ax[1].get_images()[0] self.ax[1].set_title('Spectrogram') self.ax[1].grid(color='w', linewidth=0.5) self.ax[1].set_xticks([]) self.ax[1].set_ylabel('kHz') bbox_orig = patches.Rectangle((0,0),0,0, edgecolor='w', linewidth=0, fill=False) self.ax[1].add_patch(bbox_orig) self.annot = self.ax[0].annotate('', xy=(0,0), xytext=(20,20),textcoords='offset points', bbox=dict(boxstyle='round', fc='w'), arrowprops=dict(arrowstyle='->')) self.annot.set_visible(False) self.fig.canvas.mpl_connect('motion_notify_event', self.mouse_hover) self.fig.canvas.mpl_connect('key_press_event', self.key_press) def mouse_hover(self, event): vis = self.annot.get_visible() if event.inaxes == self.ax[0]: cont, ind = self.low_dim_plt.contains(event) if cont: self.current_id = ind['ind'][0] # copy spec into full window - probably a better way of doing this new_spec = self.blank_spec.copy() w_diff = (self.blank_spec.shape[1] - self.spec_slices[self.current_id].shape[1])//2 new_spec[:, w_diff:self.spec_slices[self.current_id].shape[1]+w_diff] = self.spec_slices[self.current_id] self.spec_im.set_data(new_spec) self.spec_im.set_clim(vmin=0, vmax=new_spec.max()) # draw bounding box around call self.ax[1].patches[0].remove() spec_width_orig = self.spec_slices[self.current_id].shape[1]/(1.0+2.0*self.spec_pad) xx = w_diff + self.spec_pad*spec_width_orig ww = spec_width_orig yy = self.call_info[self.current_id]['low_freq']/1000 hh = (self.call_info[self.current_id]['high_freq']-self.call_info[self.current_id]['low_freq'])/1000 bbox = patches.Rectangle((xx,yy),ww,hh, edgecolor='r', linewidth=0.5, fill=False) self.ax[1].add_patch(bbox) # update annotation arrow pos = self.low_dim_plt.get_offsets()[self.current_id] self.annot.xy = pos self.annot.set_visible(True) # write call info info_str = self.call_info[self.current_id]['file_name'] + ', time=' \ + str(round(self.call_info[self.current_id]['start_time'],3)) \ + ', prob=' + str(round(self.call_info[self.current_id]['det_prob'],3)) self.ax[0].set_xlabel(info_str) # redraw self.fig.canvas.draw_idle() def key_press(self, event): if event.key.isdigit(): self.labels_cols[self.current_id] = colors[int(event.key)] self.labels[self.current_id] = int(event.key) self.annotated[self.current_id] = 1 elif event.key == 'enter' and self.allow_training: self.train_classifier() elif event.key == 'x' and self.allow_training: self.get_classifier_params() self.ax[0].scatter(self.feats_ds[:, 0], self.feats_ds[:, 1], c=self.labels_cols, s=self.pt_size) self.fig.canvas.draw_idle() def train_classifier(self): # TODO maybe it's better to classify in 2D space - but then can't be linear ... inds = np.where(self.annotated == 1)[0] labs_un, labs_inds = np.unique(self.labels[inds], return_inverse=True) if labs_un.shape[0] > 1: # needs at least 2 classes self.clf = LinearSVC(C=1.0, penalty='l2', loss='squared_hinge', tol=0.0001, intercept_scaling=1.0, max_iter=2000) self.clf.fit(self.feats[inds, :], self.labels[inds]) # update labels inds_unlab = np.where(self.annotated == 0)[0] self.labels[inds_unlab] = self.clf.predict(self.feats[inds_unlab]) for ii in inds_unlab: self.labels_cols[ii] = colors[self.labels[ii]] else: print('Not enough data - please label more classes.') def get_classifier_params(self): res = {} if self.clf is None: print('Model not trained!') else: res['weights'] = self.clf.coef_.astype(np.float32) res['biases'] = self.clf.intercept_.astype(np.float32) return res