289 lines
8.9 KiB
Python
289 lines
8.9 KiB
Python
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# %%
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import pandas as pd
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from utils import Retriever, cosine_similarity_chunked
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import os
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import glob
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import numpy as np
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from tqdm import tqdm
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# %%
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fold = 1
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data_path = f'../../train/mapping_pattern/mapping_prediction/exports/result_group_{fold}.csv'
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test_df = pd.read_csv(data_path, skipinitialspace=True)
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# %%
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class Embedder():
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input_df: pd.DataFrame
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fold: int
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def __init__(self, input_df):
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self.input_df = input_df
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def make_embedding(self, checkpoint_path):
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def generate_input_list(df):
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input_list = []
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for _, row in df.iterrows():
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desc = f"<DESC>{row['tag_description']}<DESC>"
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unit = f"<UNIT>{row['unit']}<UNIT>"
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element = f"{desc}{unit}"
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input_list.append(element)
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return input_list
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# prepare reference embed
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train_data = list(generate_input_list(self.input_df))
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# Define the directory and the pattern
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retriever_train = Retriever(train_data, checkpoint_path)
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retriever_train.make_embedding(batch_size=64)
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return retriever_train.embeddings.to('cpu')
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# %%
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data_path = f"../../data_preprocess/exports/dataset/group_{fold}/train_all.csv"
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train_df = pd.read_csv(data_path, skipinitialspace=True)
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checkpoint_directory = "../../train/classification_bert"
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directory = os.path.join(checkpoint_directory, f'checkpoint_fold_{fold}')
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# Use glob to find matching paths
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# path is usually checkpoint_fold_1/checkpoint-<step number>
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# we are guaranteed to save only 1 checkpoint from training
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pattern = 'checkpoint-*'
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checkpoint_path = glob.glob(os.path.join(directory, pattern))[0]
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train_embedder = Embedder(input_df=train_df)
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train_embeds = train_embedder.make_embedding(checkpoint_path)
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test_embedder = Embedder(input_df=test_df)
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test_embeds = test_embedder.make_embedding(checkpoint_path)
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# %%
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# test embeds are inputs since we are looking back at train data
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cos_sim_matrix = cosine_similarity_chunked(test_embeds, train_embeds, chunk_size=1024).cpu().numpy()
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# %%
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# the following function takes in a full cos_sim_matrix
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# condition_source: boolean selectors of the source embedding
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# condition_target: boolean selectors of the target embedding
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def find_closest(cos_sim_matrix, condition_source, condition_target):
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# subset_matrix = cos_sim_matrix[condition_source]
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# except we are subsetting 2D matrix (row, column)
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subset_matrix = cos_sim_matrix[np.ix_(condition_source, condition_target)]
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# we select top k here
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# Get the indices of the top 5 maximum values along axis 1
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top_k = 3
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top_k_indices = np.argsort(subset_matrix, axis=1)[:, -top_k:] # Get indices of top k values
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# note that top_k_indices is a nested list because of the 2d nature of the matrix
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# the result is flipped
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top_k_indices[0] = top_k_indices[0][::-1]
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# Get the values of the top 5 maximum scores
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top_k_values = np.take_along_axis(subset_matrix, top_k_indices, axis=1)
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return top_k_indices, top_k_values
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####################################################
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# special find-back code
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# %%
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def find_back_element_with_print(select_idx):
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condition_source = test_df['tag_description'] == test_df[test_df.index == select_idx]['tag_description'].tolist()[0]
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condition_target = np.ones(train_embeds.shape[0], dtype=bool)
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top_k_indices, top_k_values = find_closest(
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cos_sim_matrix=cos_sim_matrix,
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condition_source=condition_source,
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condition_target=condition_target)
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training_data_pattern_list = train_df.iloc[top_k_indices[0]]['pattern'].to_list()
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training_desc_list = train_df.iloc[top_k_indices[0]]['tag_description'].to_list()
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test_data_pattern_list = test_df[test_df.index == select_idx]['pattern'].to_list()
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test_desc_list = test_df[test_df.index == select_idx]['tag_description'].to_list()
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test_ship_id = test_df[test_df.index == select_idx]['ships_idx'].to_list()[0]
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predicted_test_data = test_df[test_df.index == select_idx]['p_thing'] + ' ' + test_df[test_df.index == select_idx]['p_property']
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predicted_test_data = predicted_test_data.to_list()[0]
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print("*" * 80)
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print("idx:", select_idx)
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print("train desc", training_desc_list)
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print("train thing+property", training_data_pattern_list)
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print("test desc", test_desc_list)
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print("test thing+property", test_data_pattern_list)
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print("predicted thing+property", predicted_test_data)
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print("ships idx", test_ship_id)
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print("score:", top_k_values[0])
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test_pattern = test_data_pattern_list[0]
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find_back_list = [ test_pattern in pattern for pattern in training_data_pattern_list ]
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if sum(find_back_list) > 0:
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return True
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else:
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return False
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# %%
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def find_back_element(select_idx):
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in_train_flag = False
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condition_source = test_df['tag_description'] == test_df[test_df.index == select_idx]['tag_description'].tolist()[0]
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condition_target = np.ones(train_embeds.shape[0], dtype=bool)
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top_k_indices, top_k_values = find_closest(
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cos_sim_matrix=cos_sim_matrix,
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condition_source=condition_source,
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condition_target=condition_target)
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training_data_pattern_list = train_df.iloc[top_k_indices[0]]['pattern'].to_list()
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test_data_pattern_list = test_df[test_df.index == select_idx]['pattern'].to_list()
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# just to convert the series format to string
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test_pattern = test_data_pattern_list[0]
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# print(training_data_pattern_list)
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# print(test_data_pattern_list)
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find_back_list = [ test_pattern in pattern for pattern in training_data_pattern_list ]
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if sum(find_back_list) > 0:
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in_train_flag = True
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else:
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in_train_flag = False
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return in_train_flag, top_k_values[0][0]
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# %%
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in_train_list = []
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sim_list = []
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for select_idx in tqdm(test_df.index):
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in_train_flag, top_sim_value = find_back_element(select_idx)
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in_train_list.append(in_train_flag)
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sim_list.append(top_sim_value)
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# analysis 1: using threshold to perform find-back prediction success
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# %%
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threshold = 0.9
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predict_list = [ elem > threshold for elem in sim_list ]
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# %%
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from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix
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y_true = in_train_list
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y_pred = predict_list
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# Compute metrics
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accuracy = accuracy_score(y_true, y_pred)
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f1 = f1_score(y_true, y_pred, average='macro')
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precision = precision_score(y_true, y_pred, average='macro')
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recall = recall_score(y_true, y_pred, average='macro')
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# Print the results
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print(f'Accuracy: {accuracy:.5f}')
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print(f'F1 Score: {f1:.5f}')
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print(f'Precision: {precision:.5f}')
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print(f'Recall: {recall:.5f}')
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# analysis 2: using find-back class to check distribution of similarities
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# %%
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sim_list_true = []
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sim_list_false = []
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for idx, elem in enumerate(in_train_list):
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# true condition
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if elem:
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sim_list_true.append(sim_list[idx])
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else:
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sim_list_false.append(sim_list[idx])
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# %%
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import matplotlib.pyplot as plt
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# Sample data
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list1 = sim_list_true
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list2 = sim_list_false
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# Plot histograms
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bins = 50
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plt.hist(list1, bins=bins, alpha=0.5, label='List 1', density=False)
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plt.hist(list2, bins=bins, alpha=0.5, label='List 2', density=False)
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# Labels and legend
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plt.xlabel('Value')
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plt.ylabel('Frequency')
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plt.legend(loc='upper right')
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plt.title('Histograms of in-dist and out-dist similarities')
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# Show plot
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plt.show()
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# analysis 3
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# MDM result
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# MDM is not an accurate measure due to inconsistencies in training and test
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# distributions
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# e.g. training is a subset of MDM data, but test could contain MDM data not
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# found in train, therefore we cannot possibly achieve perfect prediction of
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# 'MDM' data
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# it is more accurate to use the result obtained from the find-back search
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# %%
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# there are 2183 actual datasets
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sum(test_df['MDM'])
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# %%
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# we find 3079 to be similar to the training distribution
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sum(predict_list)
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# %%
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# in actuality only 2051 are similar to the training distribution enough to find
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# answers during find-back
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sum(in_train_list)
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# %%
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# out of predicted, 1947 are mdm
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# by setting a threshold, we are able to get 95% of 2051
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sum(test_df[predict_list]['MDM'])
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# %%
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# out of find-back labels, 2051 are mdm
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# this represents the limit of the data distributional differences
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sum(test_df[in_train_list]['MDM'])
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# analysis 4
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# check if similarity is different between mdm and non-mdm
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# this also checks the validity of the selection approach
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# %%
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sim_list_true = []
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sim_list_false = []
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in_mdm_list = test_df['MDM'].to_list()
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for idx, elem in enumerate(in_mdm_list):
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# true condition
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if elem:
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sim_list_true.append(sim_list[idx])
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else:
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sim_list_false.append(sim_list[idx])
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# %%
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import matplotlib.pyplot as plt
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# Sample data
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list1 = sim_list_true
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list2 = sim_list_false
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# Plot histograms
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bins = 50
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plt.hist(list1, bins=bins, alpha=0.5, label='List 1', density=False)
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plt.hist(list2, bins=bins, alpha=0.5, label='List 2', density=False)
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# Labels and legend
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plt.xlabel('Value')
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plt.ylabel('Frequency')
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plt.legend(loc='upper right')
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plt.title('Histograms of in-dist and out-dist similarities')
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# Show plot
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plt.show()
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# %%
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