Sourcery refactored master branch #1
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| x=resp.json() | ||
| j = json.loads(x) | ||
| d = dict(j) | ||
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| for k,v in (d.items()): | ||
| print("{}: {}".format(k,round(v,2))) | ||
| print(f"{k}: {round(v, 2)}") |
Author
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Lines 30-36 refactored with the following changes:
- Replace call to format with f-string (
use-fstring-for-formatting)
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| clf = 'lm_model_v1.pk' | ||
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| if test.empty: | ||
| return(bad_request()) | ||
| else: | ||
| #Load the saved model | ||
| print("Loading the model...") | ||
| loaded_model = None | ||
| with open('./models/'+clf,'rb') as f: | ||
| loaded_model = pickle.load(f) | ||
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| print("The model has been loaded...doing predictions now...") | ||
| print() | ||
| predictions = loaded_model.predict(test) | ||
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| prediction_series = pd.Series(predictions) | ||
| response = jsonify(prediction_series.to_json()) | ||
| response.status_code = 200 | ||
| return (response) | ||
| #Load the saved model | ||
| print("Loading the model...") | ||
| loaded_model = None | ||
| clf = 'lm_model_v1.pk' | ||
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| with open(f'./models/{clf}', 'rb') as f: | ||
| loaded_model = pickle.load(f) | ||
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| print("The model has been loaded...doing predictions now...") | ||
| print() | ||
| predictions = loaded_model.predict(test) | ||
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| prediction_series = pd.Series(predictions) | ||
| response = jsonify(prediction_series.to_json()) | ||
| response.status_code = 200 | ||
| return (response) |
Author
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Function apicall refactored with the following changes:
- Move assignments closer to their usage (
move-assign) - Remove unnecessary else after guard condition (
remove-unnecessary-else) - Use f-string instead of string concatenation (
use-fstring-for-concatenation)
| import pandas as pd | ||
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| import os | ||
| import os |
Author
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Lines 4-97 refactored with the following changes:
- Use f-string instead of string concatenation [×5] (
use-fstring-for-concatenation) - Replace call to format with f-string (
use-fstring-for-formatting) - Hoist repeated code outside conditional statement (
hoist-statement-from-if) - Replace a[0:x] with a[:x] and a[x:len(a)] with a[x:] [×2] (
remove-redundant-slice-index) - Hoist nested repeated code outside conditional statements (
hoist-similar-statement-from-if)
| # Keep adding new words | ||
| for i in range(new_words): | ||
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| for _ in range(new_words): |
Author
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Function generate_random_start refactored with the following changes:
- Replace unused for index with underscore (
for-index-underscore) - Convert for loop into list comprehension [×2] (
list-comprehension) - Inline variable that is immediately returned (
inline-immediately-returned-variable) - Use f-string instead of string concatenation [×6] (
use-fstring-for-concatenation)
This removes the following comments ( why? ):
#return f"<div>{seed_html}</div><div>{gen_html}</div><div>{a_html}</div>"
# Showing generated and actual abstract
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| word_idx = json.load(open('data/word-index.json')) | ||
| word_idx = json.load(open('data/word-index.json')) |
Author
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Function generate_from_seed refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable) - Use f-string instead of string concatenation [×2] (
use-fstring-for-concatenation)
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| for i in range(len(y)): | ||
| for _ in range(len(y)): |
Author
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Function flip refactored with the following changes:
- Replace unused for index with underscore (
for-index-underscore)
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| if m==None: | ||
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| if m is None: | ||
| m='' | ||
| for i in range(1,n_features+1): | ||
| c='x'+str(i) | ||
| c = f'x{str(i)}' | ||
| c+=np.random.choice(['+','-'],p=[0.5,0.5]) | ||
| m+=c | ||
| m=m[:-1] | ||
| sym_m=sympify(m) | ||
| n_features=len(sym_m.atoms(Symbol)) | ||
| evals=[] | ||
| lst_features=[] | ||
| for i in range(n_features): | ||
| lst_features.append(np.random.normal(scale=5,size=n_samples)) | ||
| lst_features = [ | ||
| np.random.normal(scale=5, size=n_samples) for _ in range(n_features) | ||
| ] | ||
| lst_features=np.array(lst_features) | ||
| lst_features=lst_features.T | ||
| for i in range(n_samples): | ||
| evals.append(eval_multinomial(m,vals=list(lst_features[i]))) | ||
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| evals = [ | ||
| eval_multinomial(m, vals=list(lst_features[i])) | ||
| for i in range(n_samples) | ||
| ] | ||
| evals=np.array(evals) | ||
| evals_binary=evals>0 | ||
| evals_binary=evals_binary.flatten() | ||
| evals_binary=np.array(evals_binary,dtype=int) | ||
| evals_binary=flip(evals_binary,p=flip_y) | ||
| evals_binary=evals_binary.reshape(n_samples,1) | ||
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| lst_features=lst_features.reshape(n_samples,n_features) | ||
| x=np.hstack((lst_features,evals_binary)) | ||
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| return (x) | ||
| return np.hstack((lst_features,evals_binary)) |
Author
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Function gen_classification_symbolic refactored with the following changes:
- Use x is None rather than x == None (
none-compare) - Convert for loop into list comprehension [×2] (
list-comprehension) - Replace unused for index with underscore (
for-index-underscore) - Inline variable that is immediately returned (
inline-immediately-returned-variable) - Move assignment closer to its usage within a block (
move-assign-in-block) - Use f-string instead of string concatenation (
use-fstring-for-concatenation)
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| if m==None: | ||
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| if m is None: | ||
| m='' | ||
| for i in range(1,n_features+1): | ||
| c='x'+str(i) | ||
| c = f'x{str(i)}' | ||
| c+=np.random.choice(['+','-'],p=[0.5,0.5]) | ||
| m+=c | ||
| m=m[:-1] | ||
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| sym_m=sympify(m) | ||
| n_features=len(sym_m.atoms(Symbol)) | ||
| evals=[] | ||
| lst_features=[] | ||
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| for i in range(n_features): | ||
| lst_features.append(np.random.normal(scale=5,size=n_samples)) | ||
| lst_features = [ | ||
| np.random.normal(scale=5, size=n_samples) for _ in range(n_features) | ||
| ] | ||
| lst_features=np.array(lst_features) | ||
| lst_features=lst_features.T | ||
| lst_features=lst_features.reshape(n_samples,n_features) | ||
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| for i in range(n_samples): | ||
| evals.append(eval_multinomial(m,vals=list(lst_features[i]))) | ||
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| evals = [ | ||
| eval_multinomial(m, vals=list(lst_features[i])) | ||
| for i in range(n_samples) | ||
| ] | ||
| evals=np.array(evals) | ||
| evals=evals.reshape(n_samples,1) | ||
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Author
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Function gen_regression_symbolic refactored with the following changes:
- Use x is None rather than x == None (
none-compare) - Convert for loop into list comprehension [×2] (
list-comprehension) - Replace unused for index with underscore (
for-index-underscore) - Inline variable that is immediately returned (
inline-immediately-returned-variable) - Move assignment closer to its usage within a block (
move-assign-in-block) - Use f-string instead of string concatenation (
use-fstring-for-concatenation)
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| df = pd.DataFrame(np.random.normal(loc=5, | ||
| scale=5, size=50).reshape(10, 5), | ||
| columns = ['A'+ str(i) for i in range(1, 6)]) | ||
| df = pd.DataFrame( | ||
| np.random.normal(loc=5, scale=5, size=50).reshape(10, 5), | ||
| columns=[f'A{str(i)}' for i in range(1, 6)], | ||
| ) |
Author
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Lines 280-317 refactored with the following changes:
- Use f-string instead of string concatenation (
use-fstring-for-concatenation) - Simplify comparison to string length [×2] (
simplify-str-len-comparison)
| ``` | ||
| x = st.slider('x', -8, 8) | ||
| """ | ||
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Author
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Lines 414-414 refactored with the following changes:
- Use f-string instead of string concatenation (
use-fstring-for-concatenation)
| s3=sympify(s2) | ||
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| return(s3) | ||
| return sympify(s2) |
Author
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Function symbolize refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| sym_lst=[] | ||
| for s in sym_set: | ||
| sym_lst.append(str(s)) | ||
| sym_lst = [str(s) for s in sym_set] |
Author
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Function eval_multinomial refactored with the following changes:
- Convert for loop into list comprehension [×2] (
list-comprehension) - Remove an unnecessary list construction call prior to sorting (
skip-sorted-list-construction)
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| for i in range(len(y)): | ||
| for _ in range(len(y)): |
Author
There was a problem hiding this comment.
Function flip refactored with the following changes:
- Replace unused for index with underscore (
for-index-underscore)
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| if m==None: | ||
| if m is None: | ||
| m='' | ||
| for i in range(1,n_features+1): | ||
| c='x'+str(i) | ||
| c = f'x{str(i)}' | ||
| c+=np.random.choice(['+','-'],p=[0.5,0.5]) | ||
| m+=c | ||
| m=m[:-1] | ||
| sym_m=sympify(m) | ||
| n_features=len(sym_m.atoms(Symbol)) | ||
| evals=[] | ||
| lst_features=[] | ||
| for i in range(n_features): | ||
| lst_features.append(np.random.normal(scale=5,size=n_samples)) | ||
| lst_features = [ | ||
| np.random.normal(scale=5, size=n_samples) for _ in range(n_features) | ||
| ] | ||
| lst_features=np.array(lst_features) | ||
| lst_features=lst_features.T | ||
| for i in range(n_samples): | ||
| evals.append(eval_multinomial(m,vals=list(lst_features[i]))) | ||
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| evals = [ | ||
| eval_multinomial(m, vals=list(lst_features[i])) | ||
| for i in range(n_samples) | ||
| ] | ||
| evals=np.array(evals) | ||
| evals_binary=evals>0 | ||
| evals_binary=evals_binary.flatten() | ||
| evals_binary=np.array(evals_binary,dtype=int) | ||
| evals_binary=flip(evals_binary,p=flip_y) | ||
| evals_binary=evals_binary.reshape(n_samples,1) | ||
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| lst_features=lst_features.reshape(n_samples,n_features) | ||
| x=np.hstack((lst_features,evals_binary)) | ||
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| return (x) | ||
| return np.hstack((lst_features,evals_binary)) |
Author
There was a problem hiding this comment.
Function gen_classification_symbolic refactored with the following changes:
- Use x is None rather than x == None (
none-compare) - Convert for loop into list comprehension [×2] (
list-comprehension) - Replace unused for index with underscore (
for-index-underscore) - Inline variable that is immediately returned (
inline-immediately-returned-variable) - Move assignment closer to its usage within a block (
move-assign-in-block) - Use f-string instead of string concatenation (
use-fstring-for-concatenation)
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| if m==None: | ||
| if m is None: | ||
| m='' | ||
| for i in range(1,n_features+1): | ||
| c='x'+str(i) | ||
| c = f'x{str(i)}' | ||
| c+=np.random.choice(['+','-'],p=[0.5,0.5]) | ||
| m+=c | ||
| m=m[:-1] | ||
|
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| sym_m=sympify(m) | ||
| n_features=len(sym_m.atoms(Symbol)) | ||
| evals=[] | ||
| lst_features=[] | ||
|
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| for i in range(n_features): | ||
| lst_features.append(np.random.normal(scale=5,size=n_samples)) | ||
| lst_features = [ | ||
| np.random.normal(scale=5, size=n_samples) for _ in range(n_features) | ||
| ] | ||
| lst_features=np.array(lst_features) | ||
| lst_features=lst_features.T | ||
| lst_features=lst_features.reshape(n_samples,n_features) | ||
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| for i in range(n_samples): | ||
| evals.append(eval_multinomial(m,vals=list(lst_features[i]))) | ||
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| evals = [ | ||
| eval_multinomial(m, vals=list(lst_features[i])) | ||
| for i in range(n_samples) | ||
| ] | ||
| evals=np.array(evals) | ||
| evals=evals.reshape(n_samples,1) | ||
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Author
There was a problem hiding this comment.
Function gen_regression_symbolic refactored with the following changes:
- Use x is None rather than x == None (
none-compare) - Convert for loop into list comprehension [×2] (
list-comprehension) - Replace unused for index with underscore (
for-index-underscore) - Inline variable that is immediately returned (
inline-immediately-returned-variable) - Move assignment closer to its usage within a block (
move-assign-in-block) - Use f-string instead of string concatenation (
use-fstring-for-concatenation)
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Branch
masterrefactored by Sourcery.If you're happy with these changes, merge this Pull Request using the Squash and merge strategy.
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