commented code
This commit is contained in:
Sandro Zimmermann
parent
efe9a1afe3
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cafea4bab1
5
main.py
5
main.py
@ -1,7 +1,8 @@
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import sys
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from py.arguments import Arguments
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from py.modell import *
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from py.model import *
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# CLI argument for file path necessary
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if not sys.argv[1:]:
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print("Usage: python3 main.py <path to csv>")
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sys.exit(1)
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@ -15,6 +16,7 @@ def main():
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args.set_graph(False)
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while repeat:
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# Display settings
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print("Currently selected setting:")
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print(f"File: {args.get_file_path()}")
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print(f"Mode: {args.get_mode()}")
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@ -57,6 +59,7 @@ def main():
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print("\n")
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# apply model, function in py/model.py
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apply_model(load_dataframe(args.get_file_path()), features, score, args.get_information(), args.get_graph())
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if __name__ == "__main__":
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@ -1,46 +1,59 @@
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from enum import Enum
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# Possible modes v = vector length of (x, y) / a = absolut values, reduce spread / c = cartesian, empirical values
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class Mode(Enum):
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V = "v"
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A = "a"
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C = "c"
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# Display pandas dataframe description, head and info
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class Information(Enum):
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DISABLED = False
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ENABLED = True
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# Display countplot, heatmap and scatterplot graphs
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class Graph(Enum):
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DISABLED = False
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ENABLED = True
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# Class Arguments for accepted values
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class Arguments:
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# Constructor. Default params: "filepath", v, False, False
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def __init__(self, file_path, mode, information, graph):
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self.file_path = file_path
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self.mode = mode
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self.information = information
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self.graph = graph
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# Filepath getter
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def get_file_path(self):
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return self.file_path
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# Filepath setter
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def set_file_path(self, value):
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self.file_path = value
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# Mode getter
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def get_mode(self):
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return self.mode.value
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# Mode setter
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def set_mode(self, value):
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self.mode = Mode(value)
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# Information getter
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def get_information(self):
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return self.information.value
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# Information setter
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def set_information(self, value):
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self.information = Information(value)
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# Graph getter
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def get_graph(self):
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return self.graph.value
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# GRaph setter
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def set_graph(self, value):
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self.graph = Graph(value)
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@ -10,16 +10,17 @@ Export the dataset to "data/synthetic_shots.csv"
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---------------------------------------------------'''
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import sys
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import pandas as pd
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import numpy as np
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import csv
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args = sys.argv[1:]
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# CLI argument for amount necessary
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if not args:
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print("Usage: python3 generate_synthetic_shots.py <number of generated shots>")
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sys.exit(1)
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# Amount to generate
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n = int(args[0])
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# Area circle
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@ -36,15 +37,14 @@ A1 = 10.5 ** 2 * np.pi
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possible_values = np.linspace(-10, 10, 41) # fromn -10 to 10 with step 0.5
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xy = [(np.random.choice(possible_values), np.random.choice(possible_values)) for _ in range(n)]
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xy = [(np.random.choice(possible_values), np.random.choice(possible_values)) for _ in range(n)] # numpy generates n amounts of (x, y) coordinates
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dataset = []
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# Apply score to coordinate based on area comparison
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for i in xy:
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A = (i[0]**2 + i[1]**2) * np.pi
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#print(A)
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if A <= A10:
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dataset.append([10, i[0], i[1]])
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elif A > A10 and A <= A9:
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@ -68,6 +68,7 @@ for i in xy:
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elif A > A1:
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dataset.append([0, i[0], i[1]])
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# Write synthetic dataset in data/synthetic_shots.csv
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with open('data/synthetic_shots.csv', 'w', newline='') as csvfile:
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fieldnames = ['points', 'x', 'y']
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writer = csv.writer(csvfile)
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@ -14,6 +14,7 @@ np.seterr(divide='ignore', invalid='ignore')
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FEATURES = ["points", "x", "y"]
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# create dataframe with csv file
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def make_dataframe(transform):
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def load_dataframe(file_path):
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try:
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@ -25,20 +26,25 @@ def make_dataframe(transform):
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quit()
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return load_dataframe
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# depending on mode, [x, y] cordinates are used as feature or length of vector (x, y) [radius] is used
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def make_features(selector):
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def select(df):
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return df
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return select(selector)
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# Feature radius when mode = v
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def radius(df):
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df["radius"] = np.sqrt(df["x"]**2 + df["y"]**2)
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return df[["radius"]]
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# Feature ["x", "y"] when mode = a or c
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def xy(df):
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features = ["x", "y"]
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return df[features]
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# apply model on dataframe. Params: df = dataframe, features = function make_features, inf = True or False, graph = True or False
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def apply_model(df, features, score, inf, graph):
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# print dataframe information
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if inf:
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print(df.describe())
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@ -57,12 +63,18 @@ def apply_model(df, features, score, inf, graph):
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plt.show()
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y = pd.get_dummies(df['points'])
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X = features(df)
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X = features(df) # select which features to use radius or xy
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# Split data into 60/40 (train/test)
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)
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# Create a RandomForestClassifier with n_estimators=700
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random_forest = RandomForestClassifier(n_estimators=700, random_state=0)
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# Create a DecisionTreeClassifier
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decision_tree = DecisionTreeClassifier(random_state=0)
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# Create a KNeighborsClassifier with n_neighbors=5
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k_neighbors = KNeighborsClassifier(n_neighbors=5)
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models = {
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@ -77,13 +89,15 @@ def apply_model(df, features, score, inf, graph):
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for name, model in models.items():
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pred = model.predict(X_test.values)
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# calculate f1 with own function
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my_f1_macro_score = calc_f1_macro(y_test, pd.DataFrame(pred))
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print(f'My F1 score of {name} is {my_f1_macro_score}')
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# calculate f1 with sklearn function
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f1_sklearn = f1_score(y_test.values, pred, average='macro')
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print(f'Sklearn F1 score of {name} is {f1_sklearn}')
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score = score()
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score = score() # promt for x, y coordinates and transform score based on mode
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label_encoder = LabelEncoder()
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df["points"] = label_encoder.fit_transform(df["points"])
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@ -104,6 +118,7 @@ def calc_f1_macro(y_true, y_pred):
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f1_scores.append(score)
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return np.mean(f1_scores)
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# calc f1 score
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def calc_f1_score(y_true, y_pred):
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tp = np.sum(np.multiply([i==True for i in y_pred], y_true))
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tn = np.sum(np.multiply([i==False for i in y_pred], [not(j) for j in y_true]))
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@ -119,12 +134,15 @@ def calc_f1_score(y_true, y_pred):
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f1 = 0
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return f1
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# calc precision
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def calc_precision(tp, fp):
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return tp / (tp + fp)
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# calc recall
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def calc_recall(tp, fn):
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return tp / (tp + fn)
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# ask for x, y value and return transformed array based on mode
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def make_score_function(transform):
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def get_score_from_cli():
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try:
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