def cascade(): """ :return: Beginning of cascade classification. Still being developed. """ pred = 0 bdt = joblib.load('models/adaboost256.pkl') frame = cv2.imread('images/gradient.jpg') # X = cf.compute_chans(frame).ravel().reshape(1, -1) # X = cf.compute_chans(cv2.resize(frame, (64, 128))).ravel().reshape(1, -1) x = cf.compute_chans(cv2.resize(frame, (64, 128))).ravel() # np.random.shuffle(x) N = 100 pred = [None] * N X = np.array([x for i in range(N)]) start = time.time() for i in range(N): for t, estimator in enumerate(bdt.estimators_): pred += _samme_proba(estimator, 2, X) p_t = pred[0, 1] / (t + 1) print('p_t is: ', p_t) if p_t < -.2 and t > 8: return False return False out = casc.cascade(X, bdt) print((time.time() - start) / N) start = time.time() for i in range(N): out = bdt.predict(X) print((time.time() - start) / N) print(type(bdt))
def get_aggressive_tweets(tweets): content = [t[1] for t in tweets] id_date = [(t[0], t[2]) for t in tweets] results = [] write_out_data( tweets, '/proj/nlp/users/terra/streaming_results/correlate_tmp.csv') predictions = cascade( '/proj/nlp/users/terra/streaming_results/correlate_tmp.csv') for p in range(0, len(predictions)): if predictions[p] == 1: results.append(id_date[p]) return results
# -*- coding: utf-8 -*- """ Created on Thu Oct 16 21:49:56 2014 @author: haggertr """ from matrix_from_xls import matrix_from_xls as mfx import sys sys.path.append('c:\\code\\cascade\\') # location of module import cascade as csc #file = 'C:\\Users\\haggertr\\Desktop\\Documents\\work - OSU\\research\\WW2100\\Research\\results2\\Cascade_plots\\save tmp\\testdata.xls' #file = 'C:\\code\\matrix-from-data\\testdata.xls' file = 'C:\\code\\matrix-from-data\\WS1_CO2.xls' #file = '1e__IjqMLBBVLqBHWZiIORkPbz6W8PQlSPVeodQBM8Oc' #google key. Add kwarg filetype='gsheet' a = mfx(file,1,48,16) b = csc.cascade(a) print b
def train(self, labeledPoints): labeledPoints = cascade(labeledPoints, self._reduce, self.nmax) X, y = self._readiterator(labeledPoints) self.model = self.create_model() self.model.fit(X, y)
def cascadeCallback(self, sender): cascade()
G = nx.from_pandas_edgelist(df, 'from', 'to', create_using=nx.DiGraph()) #%% Launch Cascade Model from cascade import cascade import random df = dfRaw[94000:94100] # Assign Random Seed Vertices n = 2 seeds = random.sample(list(set(df['from'])), n) #seeds = init_seeds; # Run Cascade Model df, df_inf, steps = cascade(seeds, df) #%% # Rename 'edgelist' to dataframe #df = edgelist; # Handle edgelist unweighted networks with uniform weights # Assign likelihood of adoption for all vertices df['willingness'] = df['willingness'].apply(lambda x: random.random()) df['influenced'] = np.zeros((len(df), 1)) # Assign "influenced" values of 10 to seeded nodes df['influenced'] = np.where(df['from'].isin(seeds), 10, 0) steps = 0
def train(self, labeledPoints): labeledPoints = cascade(labeledPoints, self._reduce, self.nmax) self.X, y = self._readiterator(labeledPoints) # final model does not need regularization self.model, self.support = self._fit(self.X, y, newC=self.C * 10.0)
for k in [5]: for method in ['greedy', 'random', 'out-degree', 'closeness centrality']: print(str(k)) start = time.time() if method == 'greedy': A = greedy(k, p, 50, G, nodes, edges) if method == 'random': A = set(random.sample(nodes, k)) if method == 'closeness centrality': A = centrality(G, k) if method == 'out-degree': A = out_degree(G, k) end = time.time() #test results# nodes_reached, GX = cascade(G, A, p) out_file.write('%s\t%s\t%f\t%d\t%d\n' % (method, str(A), end - start, k, nodes_reached)) out_file.flush() out_file.close()
def on_evolving_train_config_activate(self, radiobutton, data=None): """For showing window for configuring parameters related to cascade Training.""" wcascade = cascade()
def on_evolving_train_config_activate(self,radiobutton,data=None): """For showing window for configuring parameters related to cascade Training.""" wcascade=cascade()