<jupyter_start><jupyter_text>Leonard et al(2011)The sensitivity and specificity of the rule proposed by Leonard et al(2011) in three control groups<jupyter_code>Leonard = ['AlteredMentalStatus','FocalNeuroFindings','PainNeck',
           'Torticollis','SubInj_TorsoTrunk',
           'Predisposed','HighriskDiving','HighriskMVC']<jupyter_output><empty_output><jupyter_text>Decision treesAll models are trained on training set, and evaluate on tuning set.The plots shows ROC curves for four different classifiers with age stratification:- CART- CART with max_depth = 5- Random Forest- Simple tree: greedily search high risk features (direcly use the form of decision rule in Leonard(2011) as model)And the dashed lines mark the FPR and TPR of baselines.(Baselines are training performance)Notice: TPR = sensitivity, FPR = 1-specificity  Age > 12<jupyter_code>data_list = df_ran
data_train = data_preparation(df_ran[0],av = False, age = '>12', outside = False)
data_tuning = data_preparation(df_ran[1],av = False, age = '>12', outside = False)

frames = [data_train,data_tuning]
result = pd.concat(frames)
result.shape
data_train.shape
# control group: random

# data preparation --------------
data_list = df_ran
data_train = data_preparation(df_ran[0],av = False, age = '>12', outside = False)
data_tuning = data_preparation(df_ran[1],av = False, age = '>12', outside = False)
print(data_train.shape, data_tuning.shape)

print(data_tuning.shape)
print(evaluate_vlist(data_tuning, Leonard))
print(data_train.shape)
print(evaluate_vlist(data_train, Leonard))

Leo_evaluation = evaluate_vlist(data_tuning, Leonard)
Leo_evaluation_t = evaluate_vlist(data_train, Leonard)

X = data_train.drop(['outcome'],axis=1).copy()
y = data_train['outcome'].astype('int')
X_tuning = data_tuning.drop(['outcome'],axis=1).copy()
y_tuning = data_tuning['outcome'].astype('int')

# fiting models ---------------
cart = tree.DecisionTreeClassifier(random_state = 1)
cart = cart.fit(X,y)
cart_small = tree.DecisionTreeClassifier(max_depth = 3,random_state = 1)
cart_small = cart_small.fit(X,y)
rf = RandomForestClassifier(n_estimators = 200,random_state = 1)
rf = rf.fit(X,y)

data_train_s = data_train
results = simple_tree([data_train_s,data_tuning], tree_method = 'one', select_method = "gini")
v = results[0]
e_training = results[1]
e_tuning = results[2]

fig, ax = plt.subplots(figsize=(5.5,4))
ax.axhline(Leo_evaluation[0],linestyle = ":",color = 'blue')
ax.axvline(1-Leo_evaluation[1],linestyle = ":",color = 'blue')
sns.lineplot(x='FPR', y= 'TPR', data = e_tuning, marker='d').set(title = 'Age >= 12, Validation Set ROC curve')
RocCurveDisplay.from_estimator(rf, X_tuning, y_tuning, ax=ax, name = "RandomForest")
RocCurveDisplay.from_estimator(cart, X_tuning, y_tuning, ax=ax, name = "CART")
RocCurveDisplay.from_estimator(cart_small, X_tuning, y_tuning, ax=ax, name = "CART Pruned")
fig.tight_layout()
plt.savefig('/Users/Yaxuan/Github/stat-215-final/csi-pecarn-rule-vetting/rulevetting/projects/csi_pecarn/notebooks/figs/tuning12+.pdf')
plt.show()
, 
fig, ax = plt.subplots(figsize=(5.5,4))
ax.axhline(Leo_evaluation_t[0],linestyle = ":",color = 'blue')
ax.axvline(1-Leo_evaluation_t[1],linestyle = ":",color = 'blue')
sns.lineplot(x='FPR', y= 'TPR', data = e_training, marker='d').set(title = 'Age >= 12, Training Set ROC curve')
RocCurveDisplay.from_estimator(rf, X, y, ax=ax, name = "RandomForest")
RocCurveDisplay.from_estimator(cart, X, y, ax=ax, name = "CART")
RocCurveDisplay.from_estimator(cart_small, X, y, ax=ax, name = "CART Pruned")
fig.tight_layout()
plt.savefig('/Users/Yaxuan/Github/stat-215-final/csi-pecarn-rule-vetting/rulevetting/projects/csi_pecarn/notebooks/figs/training12+.pdf')
plt.show()

df = pd.DataFrame(data = {'variable': v, 'TPR' :e_tuning['TPR'], 'FPR' :e_tuning['FPR'] })
# df.to_csv('/Users/Yaxuan/Github/stat-215-final/csi-pecarn-rule-vetting/rulevetting/projects/csi_pecarn/notebooks/figs/12+vlist.csv')
# control group: random

# data preparation --------------
data_list = df_ran
data_train = data_preparation(df_ran[0],av = False, age = '5-12', outside = False)
data_tuning = data_preparation(df_ran[1],av = False, age = '5-12', outside = False)
print(data_train.shape, data_tuning.shape)

print(data_tuning.shape)
print(evaluate_vlist(data_tuning, Leonard))
print(data_train.shape)
print(evaluate_vlist(data_train, Leonard))

Leo_evaluation = evaluate_vlist(data_tuning, Leonard)
Leo_evaluation_t = evaluate_vlist(data_train, Leonard)

X = data_train.drop(['outcome'],axis=1).copy()
y = data_train['outcome'].astype('int')
X_tuning = data_tuning.drop(['outcome'],axis=1).copy()
y_tuning = data_tuning['outcome'].astype('int')

# fiting models ---------------
cart = tree.DecisionTreeClassifier(random_state = 1)
cart = cart.fit(X,y)
cart_small = tree.DecisionTreeClassifier(max_depth = 3,random_state = 1)
cart_small = cart_small.fit(X,y)
rf = RandomForestClassifier(n_estimators = 200,random_state = 1)
rf = rf.fit(X,y)

data_train_s = data_train
results = simple_tree([data_train_s,data_tuning], tree_method = 'one', select_method = "gini")
v = results[0]
e_training = results[1]
e_tuning = results[2]

fig, ax = plt.subplots(figsize=(5.5,4))
ax.axhline(Leo_evaluation[0],linestyle = ":",color = 'blue')
ax.axvline(1-Leo_evaluation[1],linestyle = ":",color = 'blue')
sns.lineplot(x='FPR', y= 'TPR', data = e_tuning, marker='d').set(title = '5 <= Age < 12, Validation Set ROC curve')
RocCurveDisplay.from_estimator(rf, X_tuning, y_tuning, ax=ax, name = "RandomForest")
RocCurveDisplay.from_estimator(cart, X_tuning, y_tuning, ax=ax, name = "CART")
RocCurveDisplay.from_estimator(cart_small, X_tuning, y_tuning, ax=ax, name = "CART Pruned")
fig.tight_layout()
plt.savefig('/Users/Yaxuan/Github/stat-215-final/csi-pecarn-rule-vetting/rulevetting/projects/csi_pecarn/notebooks/figs/tuning5-12.pdf')
plt.show()
, 
fig, ax = plt.subplots(figsize=(5.5,4))
ax.axhline(Leo_evaluation_t[0],linestyle = ":",color = 'blue')
ax.axvline(1-Leo_evaluation_t[1],linestyle = ":",color = 'blue')
sns.lineplot(x='FPR', y= 'TPR', data = e_training, marker='d').set(title = '5 <= Age < 12, Training Set ROC curve')
RocCurveDisplay.from_estimator(rf, X, y, ax=ax, name = "RandomForest")
RocCurveDisplay.from_estimator(cart, X, y, ax=ax, name = "CART")
RocCurveDisplay.from_estimator(cart_small, X, y, ax=ax, name = "CART Pruned")
fig.tight_layout()
plt.savefig('/Users/Yaxuan/Github/stat-215-final/csi-pecarn-rule-vetting/rulevetting/projects/csi_pecarn/notebooks/figs/training5-12.pdf')
plt.show()

df = pd.DataFrame(data = {'variable': v, 'TPR' :e_tuning['TPR'], 'FPR' :e_tuning['FPR'] })
# df.to_csv('/Users/Yaxuan/Github/stat-215-final/csi-pecarn-rule-vetting/rulevetting/projects/csi_pecarn/notebooks/figs/12+vlist.csv')
# control group: random

# data preparation --------------
data_list = df_ran
data_train = data_preparation(df_ran[0],av = False, age = '2-5', outside = False)
data_tuning = data_preparation(df_ran[1],av = False, age = '2-5', outside = False)
print(data_train.shape, data_tuning.shape)

print(data_tuning.shape)
print(evaluate_vlist(data_tuning, Leonard))
print(data_train.shape)
print(evaluate_vlist(data_train, Leonard))

Leo_evaluation = evaluate_vlist(data_tuning, Leonard)
Leo_evaluation_t = evaluate_vlist(data_train, Leonard)

X = data_train.drop(['outcome'],axis=1).copy()
y = data_train['outcome'].astype('int')
X_tuning = data_tuning.drop(['outcome'],axis=1).copy()
y_tuning = data_tuning['outcome'].astype('int')

# fiting models ---------------
cart = tree.DecisionTreeClassifier(random_state = 1)
cart = cart.fit(X,y)
cart_small = tree.DecisionTreeClassifier(max_depth = 3,random_state = 1)
cart_small = cart_small.fit(X,y)
rf = RandomForestClassifier(n_estimators = 200,random_state = 1)
rf = rf.fit(X,y)

data_train_s = data_train
results = simple_tree([data_train_s,data_tuning], tree_method = 'one', select_method = "gini")
v = results[0]
e_training = results[1]
e_tuning = results[2]

fig, ax = plt.subplots(figsize=(5.5,4))
ax.axhline(Leo_evaluation[0],linestyle = ":",color = 'blue')
ax.axvline(1-Leo_evaluation[1],linestyle = ":",color = 'blue')
sns.lineplot(x='FPR', y= 'TPR', data = e_tuning, marker='d').set(title = '2 <= Age < 5, Validation Set ROC curve')
RocCurveDisplay.from_estimator(rf, X_tuning, y_tuning, ax=ax, name = "RandomForest")
RocCurveDisplay.from_estimator(cart, X_tuning, y_tuning, ax=ax, name = "CART")
RocCurveDisplay.from_estimator(cart_small, X_tuning, y_tuning, ax=ax, name = "CART Pruned")
fig.tight_layout()
plt.savefig('/Users/Yaxuan/Github/stat-215-final/csi-pecarn-rule-vetting/rulevetting/projects/csi_pecarn/notebooks/figs/tuning2-5.pdf')
plt.show()
, 
fig, ax = plt.subplots(figsize=(5.5,4))
ax.axhline(Leo_evaluation_t[0],linestyle = ":",color = 'blue')
ax.axvline(1-Leo_evaluation_t[1],linestyle = ":",color = 'blue')
sns.lineplot(x='FPR', y= 'TPR', data = e_training, marker='d').set(title = '2 <= Age < 5, Training Set ROC curve')
RocCurveDisplay.from_estimator(rf, X, y, ax=ax, name = "RandomForest")
RocCurveDisplay.from_estimator(cart, X, y, ax=ax, name = "CART")
RocCurveDisplay.from_estimator(cart_small, X, y, ax=ax, name = "CART Pruned")
fig.tight_layout()
plt.savefig('/Users/Yaxuan/Github/stat-215-final/csi-pecarn-rule-vetting/rulevetting/projects/csi_pecarn/notebooks/figs/training2-5.pdf')
plt.show()

df = pd.DataFrame(data = {'variable': v, 'TPR' :e_tuning['TPR'], 'FPR' :e_tuning['FPR'] })
# df.to_csv('/Users/Yaxuan/Github/stat-215-final/csi-pecarn-rule-vetting/rulevetting/projects/csi_pecarn/notebooks/figs/12+vlist.csv')
# control group: random

# data preparation --------------
data_list = df_ran
data_train = data_preparation(df_ran[0],av = False, age = '<2', outside = False)
data_tuning = data_preparation(df_ran[1],av = False, age = '<2', outside = False)
print(data_train.shape, data_tuning.shape)

print(data_tuning.shape)
print(evaluate_vlist(data_tuning, Leonard))
print(data_train.shape)
print(evaluate_vlist(data_train, Leonard))

Leo_evaluation = evaluate_vlist(data_tuning, Leonard)
Leo_evaluation_t = evaluate_vlist(data_train, Leonard)

X = data_train.drop(['outcome'],axis=1).copy()
y = data_train['outcome'].astype('int')
X_tuning = data_tuning.drop(['outcome'],axis=1).copy()
y_tuning = data_tuning['outcome'].astype('int')

# fiting models ---------------
cart = tree.DecisionTreeClassifier(random_state = 1)
cart = cart.fit(X,y)
cart_small = tree.DecisionTreeClassifier(max_depth = 3,random_state = 1)
cart_small = cart_small.fit(X,y)
rf = RandomForestClassifier(n_estimators = 200,random_state = 1)
rf = rf.fit(X,y)

data_train_s = data_train
results = simple_tree([data_train_s,data_tuning], tree_method = 'one', select_method = "gini")
v = results[0]
e_training = results[1]
e_tuning = results[2]

fig, ax = plt.subplots(figsize=(5.5,4))
ax.axhline(Leo_evaluation[0],linestyle = ":",color = 'blue')
ax.axvline(1-Leo_evaluation[1],linestyle = ":",color = 'blue')
sns.lineplot(x='FPR', y= 'TPR', data = e_tuning, marker='d').set(title = 'Age < 2, Validation Set ROC curve')
RocCurveDisplay.from_estimator(rf, X_tuning, y_tuning, ax=ax, name = "RandomForest")
RocCurveDisplay.from_estimator(cart, X_tuning, y_tuning, ax=ax, name = "CART")
RocCurveDisplay.from_estimator(cart_small, X_tuning, y_tuning, ax=ax, name = "CART Pruned")
fig.tight_layout()
plt.savefig('/Users/Yaxuan/Github/stat-215-final/csi-pecarn-rule-vetting/rulevetting/projects/csi_pecarn/notebooks/figs/tuning2-.pdf')
plt.show()
, 
fig, ax = plt.subplots(figsize=(5.5,4))
ax.axhline(Leo_evaluation_t[0],linestyle = ":",color = 'blue')
ax.axvline(1-Leo_evaluation_t[1],linestyle = ":",color = 'blue')
sns.lineplot(x='FPR', y= 'TPR', data = e_training, marker='d').set(title = 'Age < 2, Training Set ROC curve')
RocCurveDisplay.from_estimator(rf, X, y, ax=ax, name = "RandomForest")
RocCurveDisplay.from_estimator(cart, X, y, ax=ax, name = "CART")
RocCurveDisplay.from_estimator(cart_small, X, y, ax=ax, name = "CART Pruned")
fig.tight_layout()
plt.savefig('/Users/Yaxuan/Github/stat-215-final/csi-pecarn-rule-vetting/rulevetting/projects/csi_pecarn/notebooks/figs/training2-.pdf')
plt.show()

df = pd.DataFrame(data = {'variable': v, 'TPR' :e_tuning['TPR'], 'FPR' :e_tuning['FPR'] })
# df.to_csv('/Users/Yaxuan/Github/stat-215-final/csi-pecarn-rule-vetting/rulevetting/projects/csi_pecarn/notebooks/figs/12+vlist.csv')
data_train.columns
v<jupyter_output><empty_output><jupyter_text>5 < Age < 12<jupyter_code># control group: random

# data preparation --------------
data_list = df_ran
data_train = data_preparation(df_ran[0],av = False, age = '5-12', outside = False)
data_tuning = data_preparation(df_ran[1],av = False, age = '5-12', outside = False)
print(data_train.shape, data_tuning.shape)

print(data_tuning.shape)
print(evaluate_vlist(data_tuning, Leonard))
print(data_train.shape)
print(evaluate_vlist(data_train, Leonard))

Leo_evaluation = evaluate_vlist(data_tuning, Leonard)
Leo_evaluation_t = evaluate_vlist(data_train, Leonard)

X = data_train.drop(['outcome'],axis=1).copy()
y = data_train['outcome'].astype('int')
X_tuning = data_tuning.drop(['outcome'],axis=1).copy()
y_tuning = data_tuning['outcome'].astype('int')

# fiting models ---------------
cart = tree.DecisionTreeClassifier(random_state = 1)
cart = cart.fit(X,y)
cart_small = tree.DecisionTreeClassifier(max_depth = 3,random_state = 1)
cart_small = cart_small.fit(X,y)
rf = RandomForestClassifier(n_estimators = 200,random_state = 1)
rf = rf.fit(X,y)

data_train_s = data_train
results = simple_tree([data_train_s,data_tuning], tree_method = 'one', select_method = "gini")
v = results[0]
e_training = results[1]
e_tuning = results[2]

fig, ax = plt.subplots(figsize=(8,6))
ax.axhline(Leo_evaluation[0],linestyle = ":")
ax.axvline(1-Leo_evaluation[1],linestyle = ":",color = 'blue')
sns.lineplot(x='FPR', y= 'TPR', data = e_tuning, marker='d')
RocCurveDisplay.from_estimator(rf, X_tuning, y_tuning, ax=ax, name = "RandomForest")
RocCurveDisplay.from_estimator(cart, X_tuning, y_tuning, ax=ax, name = "CART")
RocCurveDisplay.from_estimator(cart_small, X_tuning, y_tuning, ax=ax, name = "CART Pruned")
plt.show()

fig, ax = plt.subplots(figsize=(8,6))
ax.axhline(Leo_evaluation_t[0],linestyle = ":")
ax.axvline(1-Leo_evaluation_t[1],linestyle = ":",color = 'blue')
sns.lineplot(x='FPR', y= 'TPR', data = e_training, marker='d')
RocCurveDisplay.from_estimator(rf, X, y, ax=ax, name = "RandomForest")
RocCurveDisplay.from_estimator(cart, X, y, ax=ax, name = "CART")
RocCurveDisplay.from_estimator(cart_small, X, y, ax=ax, name = "CART Pruned")
plt.show()

df = pd.DataFrame(data = {'variable': v, 'TPR' :e_tuning['TPR'], 'FPR' :e_tuning['FPR'] })
df.to_csv('/Users/Yaxuan/Github/stat-215-final/csi-pecarn-rule-vetting/rulevetting/projects/csi_pecarn/notebooks/figs/5-12vlist.csv')<jupyter_output><empty_output><jupyter_text>2 < Age < 5<jupyter_code># control group: random

# data preparation --------------
data_list = df_ran
data_train = data_preparation(df_ran[0],av = False, age = '2-5', outside = False)
data_tuning = data_preparation(df_ran[1],av = False, age = '2-5', outside = False)
print(data_train.shape, data_tuning.shape)

print(data_tuning.shape)
print(evaluate_vlist(data_tuning, Leonard))
print(data_train.shape)
print(evaluate_vlist(data_train, Leonard))

Leo_evaluation = evaluate_vlist(data_tuning, Leonard)
Leo_evaluation_t = evaluate_vlist(data_train, Leonard)

X = data_train.drop(['outcome'],axis=1).copy()
y = data_train['outcome'].astype('int')
X_tuning = data_tuning.drop(['outcome'],axis=1).copy()
y_tuning = data_tuning['outcome'].astype('int')

# fiting models ---------------
cart = tree.DecisionTreeClassifier(random_state = 1)
cart = cart.fit(X,y)
cart_small = tree.DecisionTreeClassifier(max_depth = 3,random_state = 1)
cart_small = cart_small.fit(X,y)
rf = RandomForestClassifier(n_estimators = 200,random_state = 1)
rf = rf.fit(X,y)

data_train_s = data_train
results = simple_tree([data_train_s,data_tuning], tree_method = 'one', select_method = "gini")
v = results[0]
e_training = results[1]
e_tuning = results[2]

fig, ax = plt.subplots(figsize=(8,6))
ax.axhline(Leo_evaluation[0],linestyle = ":")
ax.axvline(1-Leo_evaluation[1],linestyle = ":",color = 'blue')
sns.lineplot(x='FPR', y= 'TPR', data = e_tuning, marker='d')
RocCurveDisplay.from_estimator(rf, X_tuning, y_tuning, ax=ax, name = "RandomForest")
RocCurveDisplay.from_estimator(cart, X_tuning, y_tuning, ax=ax, name = "CART")
RocCurveDisplay.from_estimator(cart_small, X_tuning, y_tuning, ax=ax, name = "CART Pruned")
plt.show()

fig, ax = plt.subplots(figsize=(8,6))
ax.axhline(Leo_evaluation_t[0],linestyle = ":")
ax.axvline(1-Leo_evaluation_t[1],linestyle = ":",color = 'blue')
sns.lineplot(x='FPR', y= 'TPR', data = e_training, marker='d')
RocCurveDisplay.from_estimator(rf, X, y, ax=ax, name = "RandomForest")
RocCurveDisplay.from_estimator(cart, X, y, ax=ax, name = "CART")
RocCurveDisplay.from_estimator(cart_small, X, y, ax=ax, name = "CART Pruned")
plt.show()

df = pd.DataFrame(data = {'variable': v, 'TPR' :e_tuning['TPR'], 'FPR' :e_tuning['FPR'] })
df.to_csv('/Users/Yaxuan/Github/stat-215-final/csi-pecarn-rule-vetting/rulevetting/projects/csi_pecarn/notebooks/figs/2-5vlist.csv')<jupyter_output><empty_output><jupyter_text>Age < 2<jupyter_code># control group: random

# data preparation --------------
data_list = df_ran
data_train = data_preparation(df_ran[0],av = False, age = '<2', outside = False)
data_tuning = data_preparation(df_ran[1],av = False, age = '<2', outside = False)
print(data_train.shape, data_tuning.shape)

print(data_tuning.shape)
print(evaluate_vlist(data_tuning, Leonard))
print(data_train.shape)
print(evaluate_vlist(data_train, Leonard))

Leo_evaluation = evaluate_vlist(data_tuning, Leonard)
Leo_evaluation_t = evaluate_vlist(data_train, Leonard)

X = data_train.drop(['outcome'],axis=1).copy()
y = data_train['outcome'].astype('int')
X_tuning = data_tuning.drop(['outcome'],axis=1).copy()
y_tuning = data_tuning['outcome'].astype('int')

# fiting models ---------------
cart = tree.DecisionTreeClassifier(random_state = 1)
cart = cart.fit(X,y)
cart_small = tree.DecisionTreeClassifier(max_depth = 3,random_state = 1)
cart_small = cart_small.fit(X,y)
rf = RandomForestClassifier(n_estimators = 200,random_state = 1)
rf = rf.fit(X,y)

data_train_s = data_train
results = simple_tree([data_train_s,data_tuning], tree_method = 'one', select_method = "gini")
v = results[0]
e_training = results[1]
e_tuning = results[2]

fig, ax = plt.subplots(figsize=(8,6))
ax.axhline(Leo_evaluation[0],linestyle = ":")
ax.axvline(1-Leo_evaluation[1],linestyle = ":",color = 'blue')
sns.lineplot(x='FPR', y= 'TPR', data = e_tuning, marker='d')
RocCurveDisplay.from_estimator(rf, X_tuning, y_tuning, ax=ax, name = "RandomForest")
RocCurveDisplay.from_estimator(cart, X_tuning, y_tuning, ax=ax, name = "CART")
RocCurveDisplay.from_estimator(cart_small, X_tuning, y_tuning, ax=ax, name = "CART Pruned")
plt.show()

fig, ax = plt.subplots(figsize=(8,6))
ax.axhline(Leo_evaluation_t[0],linestyle = ":")
ax.axvline(1-Leo_evaluation_t[1],linestyle = ":",color = 'blue')
sns.lineplot(x='FPR', y= 'TPR', data = e_training, marker='d')
RocCurveDisplay.from_estimator(rf, X, y, ax=ax, name = "RandomForest")
RocCurveDisplay.from_estimator(cart, X, y, ax=ax, name = "CART")
RocCurveDisplay.from_estimator(cart_small, X, y, ax=ax, name = "CART Pruned")
plt.show()

df = pd.DataFrame(data = {'variable': v, 'TPR' :e_tuning['TPR'], 'FPR' :e_tuning['FPR'] })
df.to_csv('/Users/Yaxuan/Github/stat-215-final/csi-pecarn-rule-vetting/rulevetting/projects/csi_pecarn/notebooks/figs/2-vlist.csv')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1721779955092876, 'fraction_numerical': 0.01648295570524597, 'mean_word_length': 6.63717848791894, 'pattern_counts': {'":': 28, '<': 40, '<?xml version=': 0, '>': 28, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '81536', 'license': 'MIT', 'n_tokens_mistral': 7991, 'n_tokens_neox': 7787, 'n_words': 1257, 'path': 'rulevetting/projects/csi_pecarn/notebooks/classifier_yaxuan_age&com.ipynb', 'repo_name': 'wtorous/rule-vetting', 'size': '19596'}