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How to use get_predict_point method in Airtest

Best Python code snippet using Airtest

Evaluation.py

Source:Evaluation.py

...9    df = pd.DataFrame()10    df['y_xgb_test'] = y_test11    df['y_xgb_pred'] = y_pred12    kstable = ks(df, 'y_xgb_test', 'y_xgb_pred')13    get_pos_neg_cnt(y_test, y_pred, get_predict_point(kstable))14def ks(data=None, target=None, prob=None):15    data['target0'] = 1 - data[target]16    data['bucket'] = pd.qcut(data[prob], 10)17    grouped = data.groupby('bucket', as_index=False)18    kstable = pd.DataFrame()19    kstable['min_prob'] = grouped.min()[prob]20    kstable['max_prob'] = grouped.max()[prob]21    kstable['events'] = grouped.sum()[target]22    kstable['nonevents'] = grouped.sum()['target0']23    kstable = kstable.sort_values(by="min_prob", ascending=False).reset_index(drop=True)24    kstable['event_rate'] = (kstable.events / data[target].sum()).apply('{0:.2%}'.format)25    kstable['nonevent_rate'] = (kstable.nonevents / data['target0'].sum()).apply('{0:.2%}'.format)26    kstable['cum_eventrate'] = (kstable.events / data[target].sum()).cumsum()27    kstable['cum_noneventrate'] = (kstable.nonevents / data['target0'].sum()).cumsum()28    kstable['KS'] = np.round(kstable['cum_eventrate'] - kstable['cum_noneventrate'], 3) * 10029    # Formating30    kstable['cum_eventrate'] = kstable['cum_eventrate'].apply('{0:.2%}'.format)31    kstable['cum_noneventrate'] = kstable['cum_noneventrate'].apply('{0:.2%}'.format)32    kstable.index = range(1, 11)33    kstable.index.rename('Decile', inplace=True)34    pd.set_option('display.max_columns', 9)35    print(kstable)36    # Display KS37    from colorama import Fore38    print(Fore.RED + "KS is " + str(max(kstable['KS'])) + "%" + " at decile " + str(39        (kstable.index[kstable['KS'] == max(kstable['KS'])][0])))40    return kstable41# è¿åæ£è´é¢æµ array42def get_pos_neg_predict(y_test, y_pred):43    print('in %s' % sys._getframe().f_code.co_name)44    y = pd.DataFrame(np.array([list(y_test), list(y_pred)]).T, columns=['true', 'pred'])45    pos_pred = y[y['true'] == 1]['pred']46    neg_pred = y[y['true'] == 0]['pred']47    print(pos_pred.shape)48    print(neg_pred.shape)49    return pos_pred, neg_pred50def draw_pos_neg_picture(pos_pred, neg_pred):51    print('in %s' % sys._getframe().f_code.co_name)52    # ä½¿æ£è´æ ·æ¬éä¸è´ ç¨nanå¡«å53    pos_cnt = pos_pred.shape[0]54    neg_cnt = neg_pred.shape[0]55    if neg_cnt > pos_cnt:56        pos_pred_fill = np.array([np.nan] * (neg_cnt - pos_cnt))57        pos_pred_fill = np.hstack((pos_pred, pos_pred_fill))58        neg_pred_fill = neg_pred59    else:60        neg_pred_fill = np.array([np.nan] * (pos_cnt - neg_cnt))61        neg_pred_fill = np.hstack((neg_pred, neg_pred_fill))62        pos_pred_fill = pos_pred63    dist = pd.DataFrame(np.array([pos_pred_fill, neg_pred_fill]).T, columns=['good', 'bad'])64    #     dist= pd.DataFrame(np.array([[0.1,0.2,0.2,np.nan],[0.7,0.8,0.8,0.9]]).T,columns=['a','b'])65    fig, ax = plt.subplots(figsize=(15, 6))66    dist.plot.kde(ax=ax, legend=False, title='Histogram: good vs. bad')67    dist.plot.hist(density=False, ax=ax, color=['red', 'blue'], histtype='barstacked')  # density=Frueç¨äºå è½®å»68    ax.set_ylabel('Frequency')69    ax.grid(axis='sample')70    ax.set_facecolor('#d8dcd6')71    plt.xlim(0, 1)72    plt.show()73# ä½ç»æåå¸å¾ è°ç¨save_test_resultãget_test_resultãget_pos_neg_predictãdraw_pos_neg_pictureçå½æ°74def get_pos_neg_picture(y_test, y_pred):75    import ParseData76    print('in %s' % sys._getframe().f_code.co_name)77    ParseData.save_test_result(y_test, y_pred)78    y_test, y_pred = ParseData.read_test_result()79    pos_pred, neg_pred = get_pos_neg_predict(y_pred, y_test)80    draw_pos_neg_picture(pos_pred, neg_pred)81# è·å¾é¢æµååç¹ï¼ksæå¤§çç¹çæ¦çå¼ï¼82def get_predict_point(kstable):83    max_decile = kstable.index[kstable['KS'] == max(kstable['KS'])][0]84    predict_point = kstable['min_prob'][max_decile]85    return predict_point86# è·å¾æ£è´æ ·æ¬çå®ãé¢æµä¸ªæ° ä¼ å¥ä¸ºarrayæSeries87def get_pos_neg_cnt(y_true, y_pred, predict_point):88    df = pd.DataFrame()89    df['true'] = y_true90    df['pred'] = y_pred91    true_pos = df[df['true'] == 1].shape[0]92    true_neg = df[df['true'] == 0].shape[0]93    pred_pos = df[df['pred'] > predict_point].shape[0]  # ksæ¯å·¦å¼å³éåºé´94    pred_neg = df[df['pred'] <= predict_point].shape[0]95    assert true_pos + true_neg == pred_pos + pred_neg, 'çå¼åé¢æµå¼çæ°éä¸ä¸è´ï¼'96    print('å¨å®éçæ ·æ¬ä¸ï¼%dä¸ºæ£æ ·æ¬ï¼%dä¸ºè´æ ·æ¬ï¼æ£è´æ¯ä¾ä¸º%f' % (true_pos, true_neg, (true_pos / true_neg)))...

callbacks.py

Source:callbacks.py

...22        self.row=row23        self.count = count24        self.point = point25    26    def get_predict_point(self, transform_mat):27        """28        transform_mat: (batch, 6)29        """30        if self.point == 4:31            zeros=tf.zeros_like(transform_mat)[:,:1]32            # affine_transforms=(batch, 6)33            transform_mat = tf.concat([transform_mat[:,0:1], zeros, transform_mat[:,1:2], zeros, transform_mat[:,2:4]],1)34        35        #print("get_predict_point : transform_mat  ",end = "")36        #print(type(transform_mat[1]), transform_mat.shape,transform_mat) 37        #tf.print(tf.shape(transform_mat))38        transform_mat = np.array(transform_mat)39        #print("\n\nafter\n\n")40        #print(type(transform_mat[1]), transform_mat.shape,transform_mat)41        transform_mat = transform_mat.reshape((-1, 2, 3))42        my_coord = np.array([[43            [-1,-1,1],44            [ 1, 1,1],45            [ 1,-1,1],46            [-1, 1,1]47        ]])48        my_coord = my_coord.transpose((0,2,1))49        new_coord = np.matmul(transform_mat, my_coord)50        new_coord = new_coord.transpose((0,2,1))51        return new_coord52    def coord_to_int(self, coords, imgshape):53        b, ih, iw = imgshape[:3]54        n_points=coords.shape[-1] // 255        ncoords = (coords + 1.0) / 2.0 * np.array([[iw, ih]*n_points])56        ncoords = ncoords.astype(np.int32).reshape((b, -1))57        return ncoords58    def on_epoch_end(self, epoch, logs={}):59        for i, (images,labels) in enumerate(self.dataset, 1):60            if self.require_coords:61                label, coords=labels62            stn_result, transform_mat = self.stn_model(images, training=False)63            # process origin image64            images = images.numpy()[:self.row]65            images = (images*255.).astype(np.uint8)66            transform_mat = transform_mat.numpy()[:self.row]67            #print("on_epoch_end : transform_mat  ",end = "")68            #print(type(transform_mat[1]), transform_mat.shape,transform_mat) 69            #tf.print(tf.shape(transform_mat))70            pcoords = self.coord_to_int(self.get_predict_point(transform_mat), images.shape)71            if self.require_coords: 72                gcoords = self.coord_to_int(coords.numpy()[:self.row], images.shape)73            n_points = pcoords.shape[-1] // 274            for ii in range(len(images)):75                img = images[ii].copy()76                for iii in range(n_points):77                    if self.require_coords: 78                        images[ii] = cv2.circle(img, tuple(gcoords[ii,2*iii:2*(iii+1)]), 3, (int(127+128/4*iii), 0, 0), -1)79                    images[ii] = cv2.circle(img, tuple(pcoords[ii,2*iii:2*(iii+1)]), 3, (0, 0, int(127+128/4*iii)), -1)80                    81            images = np.vstack(images)82            # process stn_result83            stn_result = stn_result.numpy()[:self.row]84            stn_result = (stn_result*255.).astype(np.uint8)...

server.py

Source:server.py

...24@app.route("/predict", methods=["GET","POST"])25def predict():26    if not request.json or not 'data' in request.json:27        abort(400)28    result = get_predict_point(embeddings,29                            request.json['p0x'], 30                            request.json['p0y'],31                            request.json['p0r'],32                            request.json['p1x'],33                            request.json['p1y'],34                            request.json['p1r'])35    return result, 20036def get_predict_point(embeddings, p0x, p0y, p0r, p1x, p1y, p1r):37    #max_p0x, max_p0y, max_p1x, max_p1y = get_corners(p0x, p0y, p0r, p1x, p1y, p1r)38    39    #area_embeddings = get_embaddings_of_tiles_in_area(max_p0x, max_p0y, max_p1x, max_p1y)40    41    id0 = get_tile_id_by_geolocation(p0x, p0y)42    id1 = get_tile_id_by_geolocation(p1x, p1y)43    grapf = build_grapf(area_embeddings)44    predict_point = predict_on_tree(id0, id1, grapf)45def predict_on_tree(id0, id1, grapf):46    interpolation_size = 547    # interpolate from 185175 to 75168848    # These numbers are cherry picked. Once we train for longer, we can remove it49    begin_index = id0 #8421250    end_index = id1...

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