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How to use compare_features method in autotest
Best Python code snippet using autotest_python
results.py
Source:results.py 
1import os2import gym3import json4import random5import pickle6import numpy as np7import matplotlib.pyplot as plt8from os.path import join9from collections import OrderedDict10from scipy.stats import wasserstein_distance as wd11from scipy.spatial.distance import euclidean as ed12import sys 13tools_path = join(os.getcwd(), 'customer_behaviour/tools')14sys.path.insert(1, tools_path)15import policy_evaluation as pe16############################17dir_path = 'n_historical_events'18param = dir_path19n_new_customers = 1020sample_length = 200021resample = False22plot_only = True23update_classification = False24compare_features = False25k = 2 # The number of closest experts26N = 1 # The number of reinitializations when predicting a new customer 27############################28def main():29 if resample: input("Are you sure you want to resample all data?\nPress 'Enter' to continue...")30 data = load_data()31 fig1, ax1 = plt.subplots()32 ax1.set_xlabel(get_label_from_param(param))33 ax1.set_ylabel('Classification error (%)')34 fig2, ax2 = plt.subplots()35 ax2.set_xlabel(get_label_from_param(param))36 ax2.set_ylabel('Absolute difference (ED)') if compare_features else ax2.set_ylabel('Absolute difference (EMD)') 37 ax2.set_title('Comparison with %s new customers' % n_new_customers)38 # Det skulle också vara intressant att jämföra med det absoluta avståndet till den expert vars dummyvariabel är satt till 139 fig3, ax3 = plt.subplots()40 ax3.set_xlabel('Training steps')41 ax3.set_ylabel('Classification error (%)')42 fig4, ax4 = plt.subplots()43 ax4.set_xlabel('Training steps')44 ax4.set_ylabel('Absolute difference (ED)') if compare_features else ax4.set_ylabel('Absolute difference (EMD)') 45 ax4.set_title('Comparison with experts in training data')46 # Inkludera variansen i agentens beteende 47 # Agenten beter sig bra på populationsnivå efter, säg, 2000 epsioder men påvisa att den inte har någon varians på individnivå48 # Kan exempelvis summera avståndet från agenten till samtliga experter och visa att avståndet minskar med tiden49 # Kan också summera inbördes avstånd mellan agentens olika beteenden och visa att det ökar med tiden50 param_values = list(data.keys())51 n_params = len(param_values)52 assert param_values53 step_values = list(data[param_values[0]][0].models.keys())[:-1]54 n_step_values = len(step_values)55 colors = plt.rcParams['axes.prop_cycle'].by_key()['color']56 error_vs_param = [[] for i in range(n_params)]57 avg_dist_vs_param = [[] for i in range(n_params)]58 error_vs_step = [[[] for j in range(n_step_values)] for i in range(n_params)]59 avg_dist_vs_step = [[[] for j in range(n_step_values)] for i in range(n_params)]60 for param_value, results in data.items():61 i = param_values.index(param_value)62 for result in results:63 for n_train_steps, t in result.models.items():64 if n_train_steps == 'final':65 _, abs_diffs, errors = t66 error_vs_param[i].append(100 * np.mean(errors))67 avg_dist_vs_param[i].append(np.mean(abs_diffs))68 else:69 j = step_values.index(n_train_steps)70 _, _, errors, avg_dist = t71 error_vs_step[i][j].append(100 * np.mean(errors))72 avg_dist_vs_step[i][j].append(avg_dist)73 error_vs_param = list(map(lambda x: np.mean(x), error_vs_param))74 avg_dist_vs_param = list(map(lambda x: np.mean(x), avg_dist_vs_param))75 ax1.plot(param_values, error_vs_param)76 ax2.plot(param_values, avg_dist_vs_param)77 for i in range(n_params):78 temp1 = list(map(lambda x: np.mean(x), error_vs_step[i]))79 temp2 = list(map(lambda x: np.mean(x), avg_dist_vs_step[i]))80 ax3.plot(step_values, temp1, label=str(param_values[i]), color=colors[i])81 ax4.plot(step_values, temp2, label=str(param_values[i]), color=colors[i])82 for ax in (ax3, ax4):83 handles, labels = ax.get_legend_handles_labels()84 by_label = OrderedDict(zip(labels, handles))85 ax.legend(by_label.values(), by_label.keys())86 plt.show()87def save_data(path, sample_length, n_new_customers, compare_features):88 args = json.loads(open(join(path, 'args.txt'), 'r').read())89 n_experts = args['n_experts']90 # Sample customer data91 if args['state_rep'] == 71:92 env = pe.get_env_and_model(args, '', sample_length, only_env=True, n_experts_in_adam_basket=n_experts+n_new_customers)93 else:94 env = pe.get_env_and_model(args, '', sample_length, only_env=True)95 customer_trajectories = env.generate_expert_trajectories(96 out_dir=None, 97 n_demos_per_expert=1,98 n_experts=n_experts+n_new_customers,99 n_expert_time_steps=sample_length100 )101 expert_states = np.array(customer_trajectories['states'][:n_experts])102 expert_actions = np.array(customer_trajectories['actions'][:n_experts])103 new_states = np.array(customer_trajectories['states'][n_experts:])104 new_actions = np.array(customer_trajectories['actions'][n_experts:])105 if compare_features:106 avg_expert = get_features(expert_actions, average=True)107 experts = get_features(expert_actions)108 new_customers = get_features(new_actions)109 else:110 avg_expert = pe.get_distrib(expert_states, expert_actions)111 experts = get_distribs(expert_states, expert_actions)112 new_customers = get_distribs(new_states, new_actions)113 models = {}114 model_paths = [d for d in [x[0] for x in os.walk(path)] if d.endswith('checkpoint')]115 model_paths.sort(key=get_key_from_path)116 for mp in model_paths:117 n_train_steps = get_key_from_path(mp)118 if int(n_train_steps) <= 1000000: continue119 print('Collecting data from model saved after %s steps' % n_train_steps)120 if args['state_rep'] == 71:121 agent, abs_diffs, errors = evaluate_on_new_customers(args, mp, experts, new_customers, compare_features, n_new_customers)122 else:123 agent, abs_diffs, errors = evaluate_on_new_customers(args, mp, experts, new_customers, compare_features)124 avg_dist = evaluate_on_pop_level(args, mp, avg_expert, compare_features)125 126 models[n_train_steps] = (agent, abs_diffs, errors, avg_dist)127 # final_model_path = next((d for d in [x[0] for x in os.walk(path)] if d.endswith('finish')), None)128 # agent, abs_diffs, errors = evaluate_on_new_customers(args, final_model_path, experts, new_customers, compare_features)129 130 # models['final'] = (agent, abs_diffs, errors)131 result = Result(models)132 save_result(result, path)133def load_data():134 data = {}135 136 # Load data137 data_paths = [join(dir_path, x) for x in os.listdir(dir_path) if x.startswith('2020')]138 data_paths.sort()139 for i, path in enumerate(data_paths):140 print('Processing folder {} of {}'.format(i + 1, len(data_paths)))141 content = os.listdir(path)142 args = json.loads(open(join(path, 'args.txt'), 'r').read())143 if not (plot_only and not update_classification) and i == 0:144 env = pe.get_env_and_model(args, '', sample_length, only_env=True)145 # Sample customer data146 n_experts = args['n_experts']147 customer_trajectories = env.generate_expert_trajectories(148 out_dir=None, 149 n_demos_per_expert=1,150 n_experts=n_experts+n_new_customers,151 n_expert_time_steps=sample_length152 )153 expert_states = np.array(customer_trajectories['states'][:n_experts])154 expert_actions = np.array(customer_trajectories['actions'][:n_experts])155 new_states = np.array(customer_trajectories['states'][n_experts:])156 new_actions = np.array(customer_trajectories['actions'][n_experts:])157 if compare_features:158 avg_expert = get_features(expert_actions, average=True)159 experts = get_features(expert_actions)160 new_customers = get_features(new_actions)161 else:162 avg_expert = pe.get_distrib(expert_states, expert_actions)163 experts = get_distribs(expert_states, expert_actions)164 new_customers = get_distribs(new_states, new_actions)165 if 'result.pkl' in content and not resample:166 print('Loading saved result')167 result = load_result(path)168 if update_classification:169 print('Updating classification with k = %d' % k)170 for n_train_steps, t in result.models.items():171 agent = t[0]172 errors = compare_with_new_customers(agent, experts, new_customers, compare_features)173 l = list(t)174 l[2] = errors175 t = tuple(l)176 result.models.update({n_train_steps: t})177 else:178 if plot_only: 179 print('Ignoring')180 continue181 print('Collecting result by sampling from model')182 183 models = {}184 model_paths = [d for d in [x[0] for x in os.walk(path)] if d.endswith('checkpoint')]185 model_paths.sort(key=get_key_from_path)186 for mp in model_paths:187 n_train_steps = get_key_from_path(mp)188 if n_train_steps < 1000000: continue189 agent, abs_diffs, errors = evaluate_on_new_customers(args, mp, experts, new_customers, compare_features)190 avg_dist = evaluate_on_pop_level(args, mp, avg_expert, compare_features)191 192 models[n_train_steps] = (agent, abs_diffs, errors, avg_dist)193 # final_model_path = next((d for d in [x[0] for x in os.walk(path)] if d.endswith('finish')), None)194 # agent, abs_diffs, errors = evaluate_on_new_customers(args, final_model_path, experts, new_customers, compare_features)195 196 # models['final'] = (agent, abs_diffs, errors)197 result = Result(models)198 save_result(result, path)199 if args[param] in data:200 data[args[param]].append(result)201 else:202 data[args[param]] = [result]203 return data204def evaluate_on_pop_level(args, model_path, avg_expert, compare_features):205 n_experts = args['n_experts']206 207 env, model, obs_normalizer = pe.get_env_and_model(args, model_path, sample_length, only_env=False)208 metric = ed if compare_features else wd209 agent_states = []210 agent_actions = []211 for i in range(n_experts):212 # Initialize agent with data from ith expert213 env.model.spawn_new_customer(i)214 sample = env.case.get_sample(215 n_demos_per_expert=1, 216 n_historical_events=args['n_historical_events'], 217 n_time_steps=1000218 )219 all_data = np.hstack(sample[0]) # history, data = sample[0]220 j = np.random.randint(0, all_data.shape[1] - args['n_historical_events'])221 history = all_data[:, j:j + args['n_historical_events']]222 if args['state_rep'] == 71:223 adam_basket = np.random.permutation(env.case.adam_baskets[i])224 env.case.i_expert = i225 initial_state = env.case.get_initial_state(history, adam_basket[0])226 else:227 initial_state = env.case.get_initial_state(history, i)228 states, actions = pe.sample_from_policy(env, model, obs_normalizer, initial_state=initial_state)229 agent_states.append(states)230 agent_actions.append(actions)231 agent_states = np.array(agent_states)232 agent_actions = np.array(agent_actions)233 avg_agent = get_features(agent_actions, average=True) if compare_features else pe.get_distrib(agent_states, agent_actions)234 distance = metric(avg_agent, avg_expert)235 return distance236def compare_with_new_customers(agents, experts, new_customers, compare_features):237 errors = []238 metric = ed if compare_features else wd239 for i, nc in enumerate(new_customers):240 distances = [metric(nc, e) for e in experts]241 closest_experts = np.argsort(distances)[:k]242 n_errors = 0243 for a in agents[i]:244 distances = [metric(a, e) for e in experts]245 if np.argmin(distances) not in closest_experts:246 n_errors += 1247 errors.append(n_errors / N)248 return errors249def evaluate_on_new_customers(args, model_path, experts, new_customers, compare_features, n_new_customers=None):250 global k, N251 n_experts = args['n_experts']252 if n_new_customers is not None:253 env, model, obs_normalizer = pe.get_env_and_model(args, model_path, sample_length, only_env=False, n_experts_in_adam_basket=n_experts+n_new_customers)254 else:255 env, model, obs_normalizer = pe.get_env_and_model(args, model_path, sample_length, only_env=False)256 agents = []257 abs_diffs = []258 errors = []259 metric = ed if compare_features else wd260 for i, nc in enumerate(new_customers):261 distances = [metric(nc, e) for e in experts]262 closest_experts = np.argsort(distances)[:k]263 dummy = closest_experts[0]264 temp_agents = []265 temp_abs_diffs = []266 n_errors = 0267 seed = n_experts + i268 if args['state_rep'] == 71: 269 adam_basket = np.random.permutation(env.case.adam_baskets[seed])270 env.case.i_expert = seed271 env.model.spawn_new_customer(seed)272 sample = env.case.get_sample(273 n_demos_per_expert=1, 274 n_historical_events=args['n_historical_events'], 275 n_time_steps=1000276 )277 all_data = np.hstack(sample[0]) # history, data = sample[0]278 for l in range(N):279 j = np.random.randint(0, all_data.shape[1] - args['n_historical_events'])280 history = all_data[:, j:j + args['n_historical_events']]281 if args['state_rep'] == 71: 282 initial_state = env.case.get_initial_state(history, adam_basket[l])283 else:284 initial_state = env.case.get_initial_state(history, dummy) # We set dummy to closest expert285 states, actions = pe.sample_from_policy(env, model, obs_normalizer, initial_state=initial_state)286 states = np.array(states)287 actions = np.array(actions)288 a = get_features([actions]) if compare_features else pe.get_distrib(states, actions)289 temp_agents.append(a)290 temp_abs_diffs.append(metric(a, nc))291 distances = [metric(a, e) for e in experts]292 if np.argmin(distances) not in closest_experts:293 n_errors += 1294 agents.append(temp_agents)295 abs_diffs.append(temp_abs_diffs)296 errors.append(n_errors / N)297 return agents, abs_diffs, errors298############################299class Result():300 def __init__(self, models):301 self.models = models302def get_features(all_actions, average=False):303 features = []304 for actions in all_actions:305 avg, std = get_mean_std_purchase_freq(actions)306 # purchase_ratio = np.count_nonzero(actions) / len(actions)307 features.append([avg, std])308 if average:309 return np.mean(features, axis=0)310 else:311 return features312def get_mean_std_purchase_freq(actions):313 indices = np.argwhere(actions).reshape((-1,))314 assert indices.size > 0315 temp = np.diff(indices)316 return np.mean(temp), np.std(temp)317def get_key_from_path(path):318 temp = path.split('/')[-1]319 steps = int(temp.split('_')[0]) # training steps320 return steps321def get_distribs(all_states, all_actions):322 N = len(all_states)323 distribs = []324 for states, actions in zip(np.split(all_states, N), np.split(all_actions, N)): # Loop over individuals325 distribs.append(pe.get_distrib(states, actions))326 return distribs327def save_result(result_obj, folder):328 with open(join(folder, 'result.pkl'), 'wb') as f:329 pickle.dump(result_obj, f, pickle.HIGHEST_PROTOCOL)330def load_result(folder):331 with open(join(folder, 'result.pkl'), 'rb') as f:332 result = pickle.load(f)333 return result334def get_label_from_param(param):335 if param == 'n_historical_events':336 return 'Number of historical events'337 else:338 raise NotImplementedError339############################340if __name__ == '__main__':341 if len(sys.argv) > 1:342 path = sys.argv[1]343 save_data(path, sample_length=10000, n_new_customers=50, compare_features=False)344 else:...
compare_efel.py
Source:compare_efel.py
1from allensdk.core.nwb_data_set import NwbDataSet2import numpy as np3import matplotlib.pyplot as plt4import sys5import h5py 6import os7import efel8import pickle as pkl9os.chdir('neuron_genetic_alg')10from config import *11os.chdir('../')12rank = int(os.environ['SLURM_PROCID'])13all_features = efel.api.getFeatureNames()14np.set_printoptions(threshold=sys.maxsize)15plt.rcParams['agg.path.chunksize'] = 1000016parsed_stim_response_path = f'./allen_model_sota_model_parsed_cell_{model_num}.hdf5'17data_file = h5py.File(parsed_stim_response_path, 'r')18sweep_keys = [e.decode('ascii') for e in data_file['sweep_keys']]19def show_efel(efel_feature_dict, feature_names):20 def normalize_efel_score(cell_features, allen_features, compare_features):21 cell_feature_mean = np.mean(cell_features)22 if cell_feature_mean != 0:23 return np.array(cell_features)/cell_feature_mean, np.array(allen_features)/cell_feature_mean,\24 np.array(compare_features)/cell_feature_mean 25 else:26 return np.array(cell_features), np.array(allen_features), np.array(compare_features) 27 width=0.428 fig, ax = plt.subplots(figsize=(15, 5))29 for i, l in enumerate(feature_names):30 efel_data = efel_feature_dict[l]31 cell_features, allen_features, compare_features = efel_data["cell_features"], efel_data["allen_features"], efel_data["compare_features"]32 if cell_features is None or len(cell_features)==0:33 cell_features = [0]34 continue35 if allen_features is None or len(allen_features)==0:36 allen_features = [0]37 continue38 if compare_features is None or len(compare_features)==0:39 compare_features = [0]40 continue41 x_cell = np.ones(len(cell_features))*i + (np.random.rand(len(cell_features))*width/3.-width/3.)42 x_allen = np.ones(len(allen_features))*i + (np.random.rand(len(allen_features))*width/3.)43 x_compare = np.ones(len(compare_features))*i + (np.random.rand(len(compare_features))*width/3.+width/3.)44 cell_features, allen_features, compare_features = normalize_efel_score(cell_features, allen_features, compare_features)45 ax.scatter(x_cell, cell_features, color="black", s=25)46 ax.scatter(x_allen, allen_features, color="blue", s=25)47 ax.scatter(x_compare, compare_features, color="crimson", s=25)48 ax.set_xticks(range(len(feature_names)))49 ax.set_xticklabels(feature_names, rotation=90)50 ax.scatter([], [], color="black", label="Cell response")51 ax.scatter([], [], color="blue", label="Allen's model response")52 ax.scatter([], [], color="crimson", label="CoMPare model response")53 ax.legend(bbox_to_anchor=(1.5, 1))54 plt.show()55 56def eval_efel(feature_name, target, data, dt):57 def diff_lists(lis1, lis2):58 def safe_mean(lis):59 if np.size(lis) == 0:60 return 061 return np.mean(lis)62 if lis1 is None and lis2 is None:63 return 064 if lis1 is None:65 lis1 = [0]66 if lis2 is None:67 lis2 = [0]68 len1, len2 = len(lis1), len(lis2)69 if len1 > len2:70 lis2 = np.concatenate((lis2, np.zeros(len1 - len2)), axis=0)71 if len2 > len1:72 lis1 = np.concatenate((lis1, np.zeros(len2 - len1)), axis=0)73 return np.sqrt(safe_mean((lis1 - lis2)**2))74 time_stamps = len(target)75 time = np.cumsum([dt for i in range(time_stamps)])76 curr_trace_target, curr_trace_data = {}, {}77 stim_start, stim_end = dt, time_stamps*dt78 curr_trace_target['T'], curr_trace_data['T'] = time, time79 curr_trace_target['V'], curr_trace_data['V'] = target, data80 curr_trace_target['stim_start'], curr_trace_data['stim_start'] = [stim_start], [stim_start]81 curr_trace_target['stim_end'], curr_trace_data['stim_end'] = [stim_end], [stim_end]82 traces = [curr_trace_target, curr_trace_data]83 traces_results = efel.getFeatureValues(traces, [feature_name], raise_warnings=False)84 diff_feature = diff_lists(traces_results[0][feature_name], traces_results[1][feature_name])85 return diff_feature, traces_results86efel_data = {}87# for sweep_key in sweep_keys:88try:89 sweep_key = sweep_keys[rank]90except:91 exit()92 93if int(sweep_key) > 78:94 exit()95print(f"processing {sweep_key} sweep for {len(all_features)}")96stim_val = data_file[sweep_key+'_stimulus'][:]97cell_response = data_file[sweep_key+'_cell_response'][:]98allen_response = data_file[sweep_key+'_allen_model_response'][:]99compare_response = data_file[sweep_key+'_compare_model_response'][:]100dt_val = data_file[sweep_key+'_dt'][0]101#plot_sampled(sweep_key, stim_val, cell_response, allen_response, compare_response)102efel_feature_dict = {}103for efel_name in all_features:104 # print(f'efel name : {efel_name}')105 l2_val_allen, efel_values_allen = eval_efel(efel_name, cell_response, allen_response, dt_val)106 l2_val_compare, efel_values_compare = eval_efel(efel_name, cell_response, compare_response, dt_val)107 efel_feature_dict[efel_name] = {"cell_features": efel_values_allen[0][efel_name],\108 "allen_features": efel_values_allen[1][efel_name],\109 "compare_features": efel_values_compare[1][efel_name]}110# print('eFEL name:', efel_name)111# #print(efel_name+" value for target:", efel_values[0][efel_name])112# #print(efel_name+" value for best model response:", efel_values[1][efel_name])113# print("Euclidean Distance for Allen's Model:", l2_val_allen)114# print("Euclidean Distance for CoMParE Model:", l2_val_compare)115# print('\n')116efel_data[sweep_key] = efel_feature_dict117# show_efel(efel_feature_dict)118os.makedirs(f'./efel_data/subsets/', exist_ok=True)...
similarity_filter.py
Source:similarity_filter.py
1#!/usr/bin/env python2# David Prihoda3# Filter a Domain CSV file by comparing its feature representation to a feature representation of a different set of samples4# Will produce the original Domain CSV file where samples with distance < threshold are removed.5import argparse6import pandas as pd7import numpy as np8from sklearn.metrics.pairwise import PAIRWISE_DISTANCE_FUNCTIONS9def get_similar_index(input_features, compare_features, metric, threshold):10 """11 Get index of input samples more distant from a second set of samples than given threshold12 :param input_features: The input samples vector representation DataFrame, one sample vector per row13 :param compare_features: The compared samples vector representation DataFrame, one sample vector per row14 :param metric: Metric to use (euclidean, cosine, ...)15 :param threshold: Return index of samples with distance is bigger than the threshold16 :return: index of input samples more distant from the second set of samples than given threshold17 """18 cols = np.intersect1d(input_features.columns, compare_features.columns)19 input_features = input_features[cols]20 compare_features = compare_features[cols]21 fn = PAIRWISE_DISTANCE_FUNCTIONS[metric]22 distances = pd.DataFrame(fn(input_features.values, compare_features.values), index=input_features.index, columns=compare_features.index)23 min_distances = distances.min(axis=1)24 print('Distance stats:')25 print(min_distances.describe())26 filtered = min_distances[min_distances >= threshold]27 print('Removed {} contigs with distance < {}'.format(len(input_features)-len(filtered), threshold))28 return filtered.index29if __name__ == "__main__":30 # Parse command line31 parser = argparse.ArgumentParser()32 parser.add_argument("-i", "--input", dest="input", required=True,33 help="Input domain CSV file.", metavar="FILE")34 parser.add_argument("-f", "--feature", dest="features", required=True,35 help="Input feature matrix pickle file.", metavar="FILE")36 parser.add_argument("-c", "--compare", dest="compare", required=True,37 help="Feature matrix pickle file to compare against.", metavar="FILE")38 parser.add_argument("--metric", dest="metric", default='euclidean',39 help="Metric used to compute similarity.", metavar="STRING")40 parser.add_argument("--threshold", dest="threshold", required=True, type=float,41 help="Remove samples with distance < threshold.", metavar="FLOAT")42 parser.add_argument("-o", "--output", dest="output", required=True,43 help="Output file path.", metavar="FILE")44 options = parser.parse_args()45 domains = pd.read_csv(options.input).set_index('contig_id')46 input_features = pd.read_pickle(options.features)47 compare_features = pd.read_pickle(options.compare)48 print('Filtering {} contigs by similarity to {} contigs...'.format(len(input_features), len(compare_features)))49 index = get_similar_index(input_features, compare_features, metric=options.metric, threshold=options.threshold)50 orig_len = len(input_features)51 new_len = len(index)52 print('Remaining contigs: {}/{} = {:.1f}%'.format(new_len, orig_len, (new_len/orig_len)*100))53 filtered_domains = domains.loc[index]54 print('Saving {} domains to: {}'.format(len(filtered_domains), options.output))...
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