def preproc_hist(self): self.clean_pred(key=self.pred.dtype.names[0]) arrays = [self.hfeatures, self.pred] histarray = munge.join_rec_arrays(arrays) self.X = histarray.view((np.float, len(histarray.dtype.names))) self.nHalos = len(self.X) pdf, self.edges = self.histogram(self.X, normed=True)
def preproc_hist(self): self.clean_pred(key=self.pred.dtype.names[0]) arrays = [self.hfeatures, self.pred] histarray = munge.join_rec_arrays(arrays) histarray = histarray.view((np.float, len(histarray.dtype.names))) counts, edges = self.histogram(histarray, normed=True) self.X, self.y, self.edges = self.flattenHist(counts, edges)
def preproc_hist(self): self.clean_pred(key=self.pred.dtype.names[0]) arrays = [self.hfeatures, self.pred] histarray = munge.join_rec_arrays(arrays) histarray = histarray.view((np.float, len(histarray.dtype.names))) pdf, self.edges = self.histogram(histarray, normed=True) self.support, self.jpdf, self.edges = self.flattenHist(pdf, self.edges) self.X = np.atleast_2d(self.hfeatures.view((float, len(self.hfeatures[0])))).T self.y = self.make_labels()
def binedges(self): """ Calculate feature bin edges for feature distribution calculation """ arrays = [self.hfeatures, self.pred] histarray = munge.join_rec_arrays(arrays) histarray = histarray.view((np.float, len(histarray.dtype.names))) counts, edges = self.histogram(histarray, normed=True) X, y, edges = self.flattenHist(counts, edges) self.edges = edges
def visModel(self): f, ax = plt.subplots(2) pred = self.predict(self.X_test) histarray = np.vstack((self.X_test.T, pred.T)).T counts, edges = self.histogram(histarray) X, y, edges = self.flattenHist(counts, edges) f, ax = self.visDensity(X, y, label='Pred') arrays = [self.hfeatures, self.pred] histarray = munge.join_rec_arrays(arrays) counts, edges = self.histogram(histarray) X, y, edges = self.flattenHist(counts, edges) f, ax = self.visDensity(X, y, f=f, ax=ax, label='Truth') plt.legend()