idx int64 0 63k | question stringlengths 61 4.03k | target stringlengths 6 1.23k |
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48,000 | def bound_bboxes ( bboxes ) : group_x0 = min ( map ( lambda l : l [ x0 ] , bboxes ) ) group_y0 = min ( map ( lambda l : l [ y0 ] , bboxes ) ) group_x1 = max ( map ( lambda l : l [ x1 ] , bboxes ) ) group_y1 = max ( map ( lambda l : l [ y1 ] , bboxes ) ) return ( group_x0 , group_y0 , group_x1 , group_y1 ) | Finds the minimal bbox that contains all given bboxes |
48,001 | def bound_elems ( elems ) : group_x0 = min ( map ( lambda l : l . x0 , elems ) ) group_y0 = min ( map ( lambda l : l . y0 , elems ) ) group_x1 = max ( map ( lambda l : l . x1 , elems ) ) group_y1 = max ( map ( lambda l : l . y1 , elems ) ) return ( group_x0 , group_y0 , group_x1 , group_y1 ) | Finds the minimal bbox that contains all given elems |
48,002 | def intersect ( a , b ) : if a [ x0 ] == a [ x1 ] or a [ y0 ] == a [ y1 ] : return False if b [ x0 ] == b [ x1 ] or b [ y0 ] == b [ y1 ] : return False return a [ x0 ] <= b [ x1 ] and b [ x0 ] <= a [ x1 ] and a [ y0 ] <= b [ y1 ] and b [ y0 ] <= a [ y1 ] | Check if two rectangles intersect |
48,003 | def reading_order ( e1 , e2 ) : b1 = e1 . bbox b2 = e2 . bbox if round ( b1 [ y0 ] ) == round ( b2 [ y0 ] ) or round ( b1 [ y1 ] ) == round ( b2 [ y1 ] ) : return float_cmp ( b1 [ x0 ] , b2 [ x0 ] ) return float_cmp ( b1 [ y0 ] , b2 [ y0 ] ) | A comparator to sort bboxes from top to bottom left to right |
48,004 | def xy_reading_order ( e1 , e2 ) : b1 = e1 . bbox b2 = e2 . bbox if round ( b1 [ x0 ] ) == round ( b2 [ x0 ] ) : return float_cmp ( b1 [ y0 ] , b2 [ y0 ] ) return float_cmp ( b1 [ x0 ] , b2 [ x0 ] ) | A comparator to sort bboxes from left to right top to bottom |
48,005 | def column_order ( b1 , b2 ) : ( top , left , bottom ) = ( 1 , 2 , 3 ) if round ( b1 [ top ] ) == round ( b2 [ top ] ) or round ( b1 [ bottom ] ) == round ( b2 [ bottom ] ) : return float_cmp ( b1 [ left ] , b2 [ left ] ) return float_cmp ( b1 [ top ] , b2 [ top ] ) | A comparator that sorts bboxes first by columns where a column is made up of all bboxes that overlap then by vertical position in each column . |
48,006 | def merge_intervals ( elems , overlap_thres = 2.0 ) : overlap_thres = max ( 0.0 , overlap_thres ) ordered = sorted ( elems , key = lambda e : e . x0 ) intervals = [ ] cur = [ - overlap_thres , - overlap_thres ] for e in ordered : if e . x0 - cur [ 1 ] > overlap_thres : if cur [ 1 ] > 0.0 : intervals . append ( cur ) cu... | Project in x axis Sort by start Go through segments and keep max x1 |
48,007 | def predict_heatmap ( pdf_path , page_num , model , img_dim = 448 , img_dir = "tmp/img" ) : if not os . path . isdir ( img_dir ) : print ( "\nCreating image folder at {}" . format ( img_dir ) ) os . makedirs ( img_dir ) pdf_name = os . path . splitext ( os . path . basename ( pdf_path ) ) [ 0 ] img_path = os . path . j... | Return an image corresponding to the page of the pdf documents saved at pdf_path . If the image is not found in img_dir this function creates it and saves it in img_dir . |
48,008 | def do_intersect ( bb1 , bb2 ) : if bb1 [ 0 ] + bb1 [ 2 ] < bb2 [ 0 ] or bb2 [ 0 ] + bb2 [ 2 ] < bb1 [ 0 ] : return False if bb1 [ 1 ] + bb1 [ 3 ] < bb2 [ 1 ] or bb2 [ 1 ] + bb2 [ 3 ] < bb1 [ 1 ] : return False return True | Helper function that returns True if two bounding boxes overlap . |
48,009 | def get_bboxes ( img , mask , nb_boxes = 100 , score_thresh = 0.5 , iou_thresh = 0.2 , prop_size = 0.09 , prop_scale = 1.2 , ) : min_size = int ( img . shape [ 0 ] * prop_size * img . shape [ 1 ] * prop_size ) scale = int ( img . shape [ 0 ] * prop_scale ) img_lbl , regions = selectivesearch . selective_search ( img , ... | Uses selective search to generate candidate bounding boxes and keeps the ones that have the largest iou with the predicted mask . |
48,010 | def _print_dict ( elem_dict ) : for key , value in sorted ( elem_dict . iteritems ( ) ) : if isinstance ( value , collections . Iterable ) : print ( key , len ( value ) ) else : print ( key , value ) | Print a dict in a readable way |
48,011 | def _font_of_mention ( m ) : for ch in m : if isinstance ( ch , LTChar ) and ch . get_text ( ) . isalnum ( ) : return ( ch . fontname , _font_size_of ( ch ) ) return ( None , 0 ) | Returns the font type and size of the first alphanumeric char in the text or None if there isn t any . |
48,012 | def _allowed_char ( c ) : c = ord ( c ) if c < 0 : return False if c < 128 : return _ascii_allowed [ c ] return True | Returns whether the given unicode char is allowed in output |
48,013 | def keep_allowed_chars ( text ) : return "" . join ( " " if c == "\n" else c for c in text . strip ( ) if _allowed_char ( c ) ) | Cleans the text for output |
48,014 | def paint_path ( self , gstate , stroke , fill , evenodd , path ) : shape = "" . join ( x [ 0 ] for x in path ) prev_split = 0 for i in range ( len ( shape ) ) : if shape [ i ] == "m" and prev_split != i : self . paint_single_path ( gstate , stroke , fill , evenodd , path [ prev_split : i ] ) prev_split = i if shape [ ... | Converting long paths to small segments each time we m = Move or h = ClosePath for polygon |
48,015 | def paint_single_path ( self , gstate , stroke , fill , evenodd , path ) : if len ( path ) < 2 : return shape = "" . join ( x [ 0 ] for x in path ) pts = [ ] for p in path : for i in range ( 1 , len ( p ) , 2 ) : pts . append ( apply_matrix_pt ( self . ctm , ( p [ i ] , p [ i + 1 ] ) ) ) if self . line_only_shape . mat... | Converting a single path draw command into lines and curves objects |
48,016 | def traverse_layout ( root , callback ) : callback ( root ) if isinstance ( root , collections . Iterable ) : for child in root : traverse_layout ( child , callback ) | Tree walker and invokes the callback as it traverse pdf object tree |
48,017 | def get_near_items ( tree , tree_key ) : try : yield tree . floor_item ( tree_key ) except KeyError : pass try : yield tree . ceiling_item ( tree_key ) except KeyError : pass | Check both possible neighbors for key in a binary tree |
48,018 | def align_add ( tree , key , item , align_thres = 2.0 ) : for near_key , near_list in get_near_items ( tree , key ) : if abs ( key - near_key ) < align_thres : near_list . append ( item ) return tree [ key ] = [ item ] | Adding the item object to a binary tree with the given key while allow for small key differences close_enough_func that checks if two keys are within threshold |
48,019 | def collect_table_content ( table_bboxes , elems ) : table_contents = [ [ ] for _ in range ( len ( table_bboxes ) ) ] prev_content = None prev_bbox = None for cid , c in enumerate ( elems ) : if isinstance ( c , LTAnno ) : if prev_content is not None : prev_content . append ( c ) continue if prev_bbox is not None and i... | Returns a list of elements that are contained inside the corresponding supplied bbox . |
48,020 | def project_onto ( objs , axis , min_gap_size = 4.0 ) : if axis == "x" : axis = 0 if axis == "y" : axis = 1 axis_end = axis + 2 if axis == 0 : objs . sort ( key = lambda o : o . x0 ) else : objs . sort ( key = lambda o : o . y0 ) intervals = [ ] groups = [ ] start_i = 0 start = objs [ 0 ] . bbox [ axis ] end = objs [ 0... | Projects object bboxes onto the axis and return the unioned intervals and groups of objects in intervals . |
48,021 | def draw_rect ( self , bbox , cell_val ) : new_x0 = int ( bbox [ x0 ] ) new_y0 = int ( bbox [ y0 ] ) new_x1 = max ( new_x0 + 1 , int ( bbox [ x1 ] ) ) new_y1 = max ( new_y0 + 1 , int ( bbox [ y1 ] ) ) self . grid [ new_x0 : new_x1 , new_y0 : new_y1 ] = cell_val | Fills the bbox with the content values Float bbox values are normalized to have non - zero area |
48,022 | def parse_layout ( elems , font_stat , combine = False ) : boxes_segments = elems . segments boxes_curves = elems . curves boxes_figures = elems . figures page_width = elems . layout . width boxes = elems . mentions avg_font_pts = get_most_common_font_pts ( elems . mentions , font_stat ) width = get_page_width ( boxes ... | Parses pdf texts into a hypergraph grouped into rows and columns and then output |
48,023 | def merge_nodes ( nodes , plane , page_stat , merge_indices ) : to_be_removed = set ( ) for inner_idx in range ( len ( nodes ) ) : inner = nodes [ inner_idx ] outers = [ ] outers_indices = [ ] for outer_idx in range ( len ( nodes ) ) : outer = nodes [ outer_idx ] if outer is inner or outer in to_be_removed : continue i... | Merges overlapping nodes |
48,024 | def _get_cols ( row_content ) : cols = [ ] subcell_col = [ ] prev_bar = None for _coord , item in row_content : if isinstance ( item , LTTextLine ) : subcell_col . append ( item ) else : if prev_bar : bar_ranges = ( prev_bar , item ) col_items = subcell_col if subcell_col else [ None ] cols . extend ( [ bar_ranges , co... | Counting the number columns based on the content of this row |
48,025 | def _one_contains_other ( s1 , s2 ) : return min ( len ( s1 ) , len ( s2 ) ) == len ( s1 & s2 ) | Whether one set contains the other |
48,026 | def is_table ( self ) : if self . type_counts [ "text" ] < 6 or "figure" in self . type_counts : return False for e in self . elems : if elem_type ( e ) == "curve" and e . height * e . width > 100 : return False if ( self . sum_elem_bbox / ( self . height * self . width ) ) > self . table_area_threshold : return False ... | Count the node s number of mention al ignment in both axes to determine if the node is a table . |
48,027 | def get_grid ( self ) : mentions , lines = _split_text_n_lines ( self . elems ) mentions . sort ( key = lambda m : ( m . yc_grid , m . xc ) ) grid = Grid ( mentions , lines , self ) return grid | Standardize the layout of the table into grids |
48,028 | def lazy_load_font ( font_size = default_font_size ) : if font_size not in _font_cache : if _platform . startswith ( "darwin" ) : font_path = "/Library/Fonts/Arial.ttf" elif _platform . startswith ( "linux" ) : font_path = "/usr/share/fonts/truetype/ubuntu-font-family/UbuntuMono-R.ttf" elif _platform . startswith ( "wi... | Lazy loading font according to system platform |
48,029 | def render_debug_img ( file_name , page_num , elems , nodes = [ ] , scaler = 1 , print_segments = False , print_curves = True , print_table_bbox = True , print_text_as_rect = True , ) : height = scaler * int ( elems . layout . height ) width = scaler * int ( elems . layout . width ) debug_img , draw = create_img ( ( 0 ... | Shows an image rendering of the pdf page along with debugging info printed |
48,030 | def _partition_estimators ( n_estimators , n_jobs ) : if n_jobs == - 1 : n_jobs = min ( cpu_count ( ) , n_estimators ) else : n_jobs = min ( n_jobs , n_estimators ) n_estimators_per_job = ( n_estimators // n_jobs ) * np . ones ( n_jobs , dtype = np . int ) n_estimators_per_job [ : n_estimators % n_jobs ] += 1 starts = ... | Private function used to partition estimators between jobs . |
48,031 | def _parallel_build_estimators ( n_estimators , ensemble , X , y , cost_mat , seeds , verbose ) : n_samples , n_features = X . shape max_samples = ensemble . max_samples max_features = ensemble . max_features if ( not isinstance ( max_samples , ( numbers . Integral , np . integer ) ) and ( 0.0 < max_samples <= 1.0 ) ) ... | Private function used to build a batch of estimators within a job . |
48,032 | def _parallel_predict ( estimators , estimators_features , X , n_classes , combination , estimators_weight ) : n_samples = X . shape [ 0 ] pred = np . zeros ( ( n_samples , n_classes ) ) n_estimators = len ( estimators ) for estimator , features , weight in zip ( estimators , estimators_features , estimators_weight ) :... | Private function used to compute predictions within a job . |
48,033 | def _create_stacking_set ( estimators , estimators_features , estimators_weight , X , combination ) : n_samples = X . shape [ 0 ] valid_estimators = np . nonzero ( estimators_weight ) [ 0 ] n_valid_estimators = valid_estimators . shape [ 0 ] X_stacking = np . zeros ( ( n_samples , n_valid_estimators ) ) for e in range ... | Private function used to create the stacking training set . |
48,034 | def _fit_bmr_model ( self , X , y ) : self . f_bmr = BayesMinimumRiskClassifier ( ) X_bmr = self . predict_proba ( X ) self . f_bmr . fit ( y , X_bmr ) return self | Private function used to fit the BayesMinimumRisk model . |
48,035 | def _fit_stacking_model ( self , X , y , cost_mat , max_iter = 100 ) : self . f_staking = CostSensitiveLogisticRegression ( verbose = self . verbose , max_iter = max_iter ) X_stacking = _create_stacking_set ( self . estimators_ , self . estimators_features_ , self . estimators_weight_ , X , self . combination ) self . ... | Private function used to fit the stacking model . |
48,036 | def _evaluate_oob_savings ( self , X , y , cost_mat ) : estimators_weight = [ ] for estimator , samples , features in zip ( self . estimators_ , self . estimators_samples_ , self . estimators_features_ ) : if not np . any ( ~ samples ) : oob_pred = estimator . predict ( X [ : , features ] ) oob_savings = max ( 0 , savi... | Private function used to calculate the OOB Savings of each estimator . |
48,037 | def predict ( self , X , cost_mat = None ) : if self . n_features_ != X . shape [ 1 ] : raise ValueError ( "Number of features of the model must " "match the input. Model n_features is {0} and " "input n_features is {1}." "" . format ( self . n_features_ , X . shape [ 1 ] ) ) if self . combination in [ 'stacking' , 'st... | Predict class for X . |
48,038 | def predict_proba ( self , X ) : if self . n_features_ != X . shape [ 1 ] : raise ValueError ( "Number of features of the model must " "match the input. Model n_features is {0} and " "input n_features is {1}." "" . format ( self . n_features_ , X . shape [ 1 ] ) ) n_jobs , n_estimators , starts = _partition_estimators ... | Predict class probabilities for X . |
48,039 | def cost_sampling ( X , y , cost_mat , method = 'RejectionSampling' , oversampling_norm = 0.1 , max_wc = 97.5 ) : cost_mis = cost_mat [ : , 0 ] cost_mis [ y == 1 ] = cost_mat [ y == 1 , 1 ] wc = np . minimum ( cost_mis / np . percentile ( cost_mis , max_wc ) , 1 ) n_samples = X . shape [ 0 ] filter_ = list ( range ( n_... | Cost - proportionate sampling . |
48,040 | def _creditscoring_costmat ( income , debt , pi_1 , cost_mat_parameters ) : def calculate_a ( cl_i , int_ , n_term ) : return cl_i * ( ( int_ * ( 1 + int_ ) ** n_term ) / ( ( 1 + int_ ) ** n_term - 1 ) ) def calculate_pv ( a , int_ , n_term ) : return a / int_ * ( 1 - 1 / ( 1 + int_ ) ** n_term ) def calculate_cl ( k ,... | Private function to calculate the cost matrix of credit scoring models . |
48,041 | def predict_proba ( self , p ) : if p . size != p . shape [ 0 ] : p = p [ : , 1 ] calibrated_proba = np . zeros ( p . shape [ 0 ] ) for i in range ( self . calibration_map . shape [ 0 ] ) : calibrated_proba [ np . logical_and ( self . calibration_map [ i , 1 ] <= p , self . calibration_map [ i , 0 ] > p ) ] = self . ca... | Calculate the calibrated probabilities |
48,042 | def cross_val_score ( estimator , X , y = None , scoring = None , cv = None , n_jobs = 1 , verbose = 0 , fit_params = None , pre_dispatch = '2*n_jobs' ) : X , y = indexable ( X , y ) cv = _check_cv ( cv , X , y , classifier = is_classifier ( estimator ) ) scorer = check_scoring ( estimator , scoring = scoring ) paralle... | Evaluate a score by cross - validation |
48,043 | def _safe_split ( estimator , X , y , indices , train_indices = None ) : if hasattr ( estimator , 'kernel' ) and isinstance ( estimator . kernel , collections . Callable ) : raise ValueError ( "Cannot use a custom kernel function. " "Precompute the kernel matrix instead." ) if not hasattr ( X , "shape" ) : if getattr (... | Create subset of dataset and properly handle kernels . |
48,044 | def _score ( estimator , X_test , y_test , scorer ) : if y_test is None : score = scorer ( estimator , X_test ) else : score = scorer ( estimator , X_test , y_test ) if not isinstance ( score , numbers . Number ) : raise ValueError ( "scoring must return a number, got %s (%s) instead." % ( str ( score ) , type ( score ... | Compute the score of an estimator on a given test set . |
48,045 | def _shuffle ( y , labels , random_state ) : if labels is None : ind = random_state . permutation ( len ( y ) ) else : ind = np . arange ( len ( labels ) ) for label in np . unique ( labels ) : this_mask = ( labels == label ) ind [ this_mask ] = random_state . permutation ( ind [ this_mask ] ) return y [ ind ] | Return a shuffled copy of y eventually shuffle among same labels . |
48,046 | def check_cv ( cv , X = None , y = None , classifier = False ) : return _check_cv ( cv , X = X , y = y , classifier = classifier , warn_mask = True ) | Input checker utility for building a CV in a user friendly way . |
48,047 | def _borderlineSMOTE ( X , y , minority_target , N , k ) : n_samples , _ = X . shape neigh = NearestNeighbors ( n_neighbors = k ) neigh . fit ( X ) safe_minority_indices = list ( ) danger_minority_indices = list ( ) for i in range ( n_samples ) : if y [ i ] != minority_target : continue nn = neigh . kneighbors ( X [ i ... | Returns synthetic minority samples . |
48,048 | def fit ( self , y_true_cal = None , y_prob_cal = None ) : if self . calibration : self . cal = ROCConvexHull ( ) self . cal . fit ( y_true_cal , y_prob_cal [ : , 1 ] ) | If calibration then train the calibration of probabilities |
48,049 | def fit ( self , y_prob , cost_mat , y_true ) : if self . calibration : cal = ROCConvexHull ( ) cal . fit ( y_true , y_prob [ : , 1 ] ) y_prob [ : , 1 ] = cal . predict_proba ( y_prob [ : , 1 ] ) y_prob [ : , 0 ] = 1 - y_prob [ : , 1 ] thresholds = np . unique ( y_prob ) cost = np . zeros ( thresholds . shape ) for i i... | Calculate the optimal threshold using the ThresholdingOptimization . |
48,050 | def predict ( self , y_prob ) : y_pred = np . floor ( y_prob [ : , 1 ] + ( 1 - self . threshold_ ) ) return y_pred | Calculate the prediction using the ThresholdingOptimization . |
48,051 | def undersampling ( X , y , cost_mat = None , per = 0.5 ) : n_samples = X . shape [ 0 ] num_y1 = y . sum ( ) num_y0 = n_samples - num_y1 filter_rand = np . random . rand ( int ( num_y1 + num_y0 ) ) if num_y1 < num_y0 : num_y0_new = num_y1 * 1.0 / per - num_y1 num_y0_new_per = num_y0_new * 1.0 / num_y0 filter_0 = np . l... | Under - sampling . |
48,052 | def _node_cost ( self , y_true , cost_mat ) : n_samples = len ( y_true ) costs = np . zeros ( 2 ) costs [ 0 ] = cost_loss ( y_true , np . zeros ( y_true . shape ) , cost_mat ) costs [ 1 ] = cost_loss ( y_true , np . ones ( y_true . shape ) , cost_mat ) pi = np . array ( [ 1 - y_true . mean ( ) , y_true . mean ( ) ] ) i... | Private function to calculate the cost of a node . |
48,053 | def _calculate_gain ( self , cost_base , y_true , X , cost_mat , split ) : if cost_base == 0.0 : return 0.0 , int ( np . sign ( y_true . mean ( ) - 0.5 ) == 1 ) j , l = split filter_Xl = ( X [ : , j ] <= l ) filter_Xr = ~ filter_Xl n_samples , n_features = X . shape if np . nonzero ( filter_Xl ) [ 0 ] . shape [ 0 ] in ... | Private function to calculate the gain in cost of using split in the current node . |
48,054 | def _best_split ( self , y_true , X , cost_mat ) : n_samples , n_features = X . shape num_pct = self . num_pct cost_base , y_pred , y_prob = self . _node_cost ( y_true , cost_mat ) gains = np . zeros ( ( n_features , num_pct ) ) pred = np . zeros ( ( n_features , num_pct ) ) splits = np . zeros ( ( n_features , num_pct... | Private function to calculate the split that gives the best gain . |
48,055 | def _tree_grow ( self , y_true , X , cost_mat , level = 0 ) : if len ( X . shape ) == 1 : tree = dict ( y_pred = y_true , y_prob = 0.5 , level = level , split = - 1 , n_samples = 1 , gain = 0 ) return tree split , gain , Xl_pred , y_pred , y_prob = self . _best_split ( y_true , X , cost_mat ) n_samples , n_features = X... | Private recursive function to grow the decision tree . |
48,056 | def _nodes ( self , tree ) : def recourse ( temp_tree_ , nodes ) : if isinstance ( temp_tree_ , dict ) : if temp_tree_ [ 'split' ] != - 1 : nodes . append ( temp_tree_ [ 'node' ] ) if temp_tree_ [ 'split' ] != - 1 : for k in [ 'sl' , 'sr' ] : recourse ( temp_tree_ [ k ] , nodes ) return None nodes_ = [ ] recourse ( tre... | Private function that find the number of nodes in a tree . |
48,057 | def _classify ( self , X , tree , proba = False ) : n_samples , n_features = X . shape predicted = np . ones ( n_samples ) if tree [ 'split' ] == - 1 : if not proba : predicted = predicted * tree [ 'y_pred' ] else : predicted = predicted * tree [ 'y_prob' ] else : j , l = tree [ 'split' ] filter_Xl = ( X [ : , j ] <= l... | Private function that classify a dataset using tree . |
48,058 | def predict ( self , X ) : if self . pruned : tree_ = self . tree_ . tree_pruned else : tree_ = self . tree_ . tree return self . _classify ( X , tree_ , proba = False ) | Predict class of X . |
48,059 | def predict_proba ( self , X ) : n_samples , n_features = X . shape prob = np . zeros ( ( n_samples , 2 ) ) if self . pruned : tree_ = self . tree_ . tree_pruned else : tree_ = self . tree_ . tree prob [ : , 1 ] = self . _classify ( X , tree_ , proba = True ) prob [ : , 0 ] = 1 - prob [ : , 1 ] return prob | Predict class probabilities of the input samples X . |
48,060 | def _delete_node ( self , tree , node ) : temp_tree = copy . deepcopy ( tree ) def recourse ( temp_tree_ , del_node ) : if isinstance ( temp_tree_ , dict ) : if temp_tree_ [ 'split' ] != - 1 : if temp_tree_ [ 'node' ] == del_node : del temp_tree_ [ 'sr' ] del temp_tree_ [ 'sl' ] del temp_tree_ [ 'node' ] temp_tree_ [ '... | Private function that eliminate node from tree . |
48,061 | def _pruning ( self , X , y_true , cost_mat ) : nodes = self . _nodes ( self . tree_ . tree_pruned ) n_nodes = len ( nodes ) gains = np . zeros ( n_nodes ) y_pred = self . _classify ( X , self . tree_ . tree_pruned ) cost_base = cost_loss ( y_true , y_pred , cost_mat ) for m , node in enumerate ( nodes ) : temp_tree = ... | Private function that prune the decision tree . |
48,062 | def pruning ( self , X , y , cost_mat ) : self . tree_ . tree_pruned = copy . deepcopy ( self . tree_ . tree ) if self . tree_ . n_nodes > 0 : self . _pruning ( X , y , cost_mat ) nodes_pruned = self . _nodes ( self . tree_ . tree_pruned ) self . tree_ . n_nodes_pruned = len ( nodes_pruned ) | Function that prune the decision tree . |
48,063 | def cost_loss ( y_true , y_pred , cost_mat ) : y_true = column_or_1d ( y_true ) y_true = ( y_true == 1 ) . astype ( np . float ) y_pred = column_or_1d ( y_pred ) y_pred = ( y_pred == 1 ) . astype ( np . float ) cost = y_true * ( ( 1 - y_pred ) * cost_mat [ : , 1 ] + y_pred * cost_mat [ : , 2 ] ) cost += ( 1 - y_true ) ... | Cost classification loss . |
48,064 | def savings_score ( y_true , y_pred , cost_mat ) : y_true = column_or_1d ( y_true ) y_pred = column_or_1d ( y_pred ) n_samples = len ( y_true ) cost_base = min ( cost_loss ( y_true , np . zeros ( n_samples ) , cost_mat ) , cost_loss ( y_true , np . ones ( n_samples ) , cost_mat ) ) cost = cost_loss ( y_true , y_pred , ... | Savings score . |
48,065 | def brier_score_loss ( y_true , y_prob ) : y_true = column_or_1d ( y_true ) y_prob = column_or_1d ( y_prob ) return np . mean ( ( y_true - y_prob ) ** 2 ) | Compute the Brier score |
48,066 | def _logistic_cost_loss ( w , X , y , cost_mat , alpha ) : if w . shape [ 0 ] == w . size : return _logistic_cost_loss_i ( w , X , y , cost_mat , alpha ) else : n_w = w . shape [ 0 ] out = np . zeros ( n_w ) for i in range ( n_w ) : out [ i ] = _logistic_cost_loss_i ( w [ i ] , X , y , cost_mat , alpha ) return out | Computes the logistic loss . |
48,067 | def predict ( self , X , cut_point = 0.5 ) : return np . floor ( self . predict_proba ( X ) [ : , 1 ] + ( 1 - cut_point ) ) | Predicted class . |
48,068 | def list_tags ( userdata ) : macros = re . findall ( '@(.*?)@' , userdata ) logging . info ( 'List of available macros:' ) for macro in macros : logging . info ( '\t%r' , macro ) | List all used macros within a UserData script . |
48,069 | def handle_tags ( userdata , macros ) : macro_vars = re . findall ( '@(.*?)@' , userdata ) for macro_var in macro_vars : if macro_var == '!all_macros_export' : macro_var_export_list = [ ] for defined_macro in macros : macro_var_export_list . append ( 'export %s="%s"' % ( defined_macro , macros [ defined_macro ] ) ) mac... | Insert macro values or auto export variables in UserData scripts . |
48,070 | def retry_on_ec2_error ( self , func , * args , ** kwargs ) : exception_retry_count = 6 while True : try : return func ( * args , ** kwargs ) except ( boto . exception . EC2ResponseError , ssl . SSLError ) as msg : exception_retry_count -= 1 if exception_retry_count <= 0 : raise msg time . sleep ( 5 ) | Call the given method with the given arguments retrying if the call failed due to an EC2ResponseError . This method will wait at most 30 seconds and perform up to 6 retries . If the method still fails it will propagate the error . |
48,071 | def connect ( self , region , ** kw_params ) : self . ec2 = boto . ec2 . connect_to_region ( region , ** kw_params ) if not self . ec2 : raise EC2ManagerException ( 'Unable to connect to region "%s"' % region ) self . remote_images . clear ( ) if self . images and any ( ( 'image_name' in img and 'image_id' not in img )... | Connect to a EC2 . |
48,072 | def resolve_image_name ( self , image_name ) : scopes = [ 'self' , 'amazon' , 'aws-marketplace' ] if image_name in self . remote_images : return self . remote_images [ image_name ] for scope in scopes : logger . info ( 'Retrieving available AMIs owned by %s...' , scope ) remote_images = self . ec2 . get_all_images ( ow... | Look up an AMI for the connected region based on an image name . |
48,073 | def create_on_demand ( self , instance_type = 'default' , tags = None , root_device_type = 'ebs' , size = 'default' , vol_type = 'gp2' , delete_on_termination = False ) : name , size = self . _get_default_name_size ( instance_type , size ) if root_device_type == 'ebs' : self . images [ instance_type ] [ 'block_device_m... | Create one or more EC2 on - demand instances . |
48,074 | def create_spot_requests ( self , price , instance_type = 'default' , root_device_type = 'ebs' , size = 'default' , vol_type = 'gp2' , delete_on_termination = False , timeout = None ) : name , size = self . _get_default_name_size ( instance_type , size ) if root_device_type == 'ebs' : self . images [ instance_type ] [ ... | Request creation of one or more EC2 spot instances . |
48,075 | def check_spot_requests ( self , requests , tags = None ) : instances = [ None ] * len ( requests ) ec2_requests = self . retry_on_ec2_error ( self . ec2 . get_all_spot_instance_requests , request_ids = requests ) for req in ec2_requests : if req . instance_id : instance = self . retry_on_ec2_error ( self . ec2 . get_o... | Check status of one or more EC2 spot instance requests . |
48,076 | def cancel_spot_requests ( self , requests ) : ec2_requests = self . retry_on_ec2_error ( self . ec2 . get_all_spot_instance_requests , request_ids = requests ) for req in ec2_requests : req . cancel ( ) | Cancel one or more EC2 spot instance requests . |
48,077 | def create_spot ( self , price , instance_type = 'default' , tags = None , root_device_type = 'ebs' , size = 'default' , vol_type = 'gp2' , delete_on_termination = False , timeout = None ) : request_ids = self . create_spot_requests ( price , instance_type = instance_type , root_device_type = root_device_type , size = ... | Create one or more EC2 spot instances . |
48,078 | def _scale_down ( self , instances , count ) : i = sorted ( instances , key = lambda i : i . launch_time , reverse = True ) if not i : return [ ] running = len ( i ) logger . info ( '%d instance/s are running.' , running ) logger . info ( 'Scaling down %d instances of those.' , count ) if count > running : logger . inf... | Return a list of |count| last created instances by launch time . |
48,079 | def _configure_ebs_volume ( self , vol_type , name , size , delete_on_termination ) : root_dev = boto . ec2 . blockdevicemapping . BlockDeviceType ( ) root_dev . delete_on_termination = delete_on_termination root_dev . volume_type = vol_type if size != 'default' : root_dev . size = size bdm = boto . ec2 . blockdevicema... | Sets the desired root EBS size otherwise the default EC2 value is used . |
48,080 | def stop ( self , instances , count = 0 ) : if not instances : return if count > 0 : instances = self . _scale_down ( instances , count ) self . ec2 . stop_instances ( [ i . id for i in instances ] ) | Stop each provided running instance . |
48,081 | def terminate ( self , instances , count = 0 ) : if not instances : return if count > 0 : instances = self . _scale_down ( instances , count ) self . ec2 . terminate_instances ( [ i . id for i in instances ] ) | Terminate each provided running or stopped instance . |
48,082 | def find ( self , instance_ids = None , filters = None ) : instances = [ ] reservations = self . retry_on_ec2_error ( self . ec2 . get_all_instances , instance_ids = instance_ids , filters = filters ) for reservation in reservations : instances . extend ( reservation . instances ) return instances | Flatten list of reservations to a list of instances . |
48,083 | def load ( self , root , module_path , pkg_name ) : root = os . path . join ( root , module_path ) import_name = os . path . join ( pkg_name , module_path ) . replace ( os . sep , '.' ) for ( _ , name , _ ) in pkgutil . iter_modules ( [ root ] ) : self . modules [ name ] = import_module ( '.' + name , package = import_... | Load modules dynamically . |
48,084 | def command_line_interfaces ( self ) : interfaces = [ ] for _ , module in self . modules . items ( ) : for entry in dir ( module ) : if entry . endswith ( 'CommandLine' ) : interfaces . append ( ( module , entry ) ) return interfaces | Return the CommandLine classes from each provider . |
48,085 | def pluralize ( item ) : assert isinstance ( item , ( int , list ) ) if isinstance ( item , int ) : return 's' if item > 1 else '' if isinstance ( item , list ) : return 's' if len ( item ) > 1 else '' return '' | Nothing to see here . |
48,086 | def validate ( self ) : if not self . conf . get ( 'auth_token' ) : raise PacketManagerException ( 'The auth token for Packet is not defined but required.' ) if not self . conf . get ( 'projects' ) : raise PacketManagerException ( 'Required "projects" section is missing.' ) projects = self . conf . get ( 'projects' ) i... | Perform some basic configuration validation . |
48,087 | def print_projects ( self , projects ) : for project in projects : print ( '{}: {}' . format ( project . name , project . id ) ) | Print method for projects . |
48,088 | def print_operating_systems ( self , operating_systems ) : for _os in operating_systems : print ( '{}: {}' . format ( _os . name , _os . slug ) ) | Print method for operating systems . |
48,089 | def print_plans ( self , plans ) : for plan in plans : print ( 'Name: {} "{}" Price: {} USD' . format ( plan . name , plan . slug , plan . pricing [ 'hour' ] ) ) self . pprint ( plan . specs ) print ( '\n' ) | Print method for plans . |
48,090 | def print_facilities ( self , facilities ) : for facility in facilities : print ( '{} - ({}): {}' . format ( facility . code , facility . name , "," . join ( facility . features ) ) ) | Print method for facilities . |
48,091 | def list_devices ( self , project_id , conditions = None , params = None ) : default_params = { 'per_page' : 1000 } if params : default_params . update ( params ) data = self . api ( 'projects/%s/devices' % project_id , params = default_params ) devices = [ ] for device in self . filter ( conditions , data [ 'devices' ... | Retrieve list of devices in a project by one of more conditions . |
48,092 | def print_devices ( self , devices ) : for device in devices : print ( 'ID: {} OS: {} IP: {} State: {} ({}) Tags: {}' . format ( device . id , device . operating_system . slug , self . get_public_ip ( device . ip_addresses ) , device . state , 'spot' if device . spot_instance else 'on-demand' , device . tags ) ) | Print method for devices . |
48,093 | def filter ( criterias , devices ) : if not criterias : return devices result = [ ] for device in devices : for criteria_name , criteria_values in criterias . items ( ) : if criteria_name in device . keys ( ) : if isinstance ( device [ criteria_name ] , list ) : for criteria_value in criteria_values : if criteria_value... | Filter a device by criterias on the root level of the dictionary . |
48,094 | def get_public_ip ( addresses , version = 4 ) : for addr in addresses : if addr [ 'public' ] and addr [ 'address_family' ] == version : return addr . get ( 'address' ) return None | Return either the devices public IPv4 or IPv6 address . |
48,095 | def validate_capacity ( self , servers ) : try : return self . manager . validate_capacity ( servers ) except packet . baseapi . Error as msg : raise PacketManagerException ( msg ) | Validates if a deploy can be fulfilled . |
48,096 | def create_volume ( self , project_id , plan , size , facility , label = "" ) : try : return self . manager . create_volume ( project_id , label , plan , size , facility ) except packet . baseapi . Error as msg : raise PacketManagerException ( msg ) | Creates a new volume . |
48,097 | def attach_volume_to_device ( self , volume_id , device_id ) : try : volume = self . manager . get_volume ( volume_id ) volume . attach ( device_id ) except packet . baseapi . Error as msg : raise PacketManagerException ( msg ) return volume | Attaches the created Volume to a Device . |
48,098 | def create_demand ( self , project_id , facility , plan , operating_system , tags = None , userdata = '' , hostname = None , count = 1 ) : tags = { } if tags is None else tags hostname = self . get_random_hostname ( ) if hostname is None else hostname devices = [ ] for i in range ( 1 , count + 1 ) : new_hostname = host... | Create a new on demand device under the given project . |
48,099 | def stop ( self , devices ) : for device in devices : self . logger . info ( 'Stopping: %s' , device . id ) try : device . power_off ( ) except packet . baseapi . Error : raise PacketManagerException ( 'Unable to stop instance "{}"' . format ( device . id ) ) | Power - Off one or more running devices . |
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