tensorflow

.gitignore

@@ -1,2 +1,2 @@
-/venv
+__pycache__
-/.ipynb_checkpoints
+.ipynb_checkpoints

countries_map.py

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+countries = {1: 'Albania',
+             2: 'Antigua and Barbuda',
+             3: 'Argentina',
+             4: 'Armenia',
+             5: 'Aruba',
+             6: 'Australia',
+             7: 'Austria',
+             8: 'Azerbaijan',
+             9: 'Bahamas',
+             10: 'Bahrain',
+             11: 'Barbados',
+             12: 'Belarus',
+             13: 'Belgium',
+             14: 'Belize',
+             15: 'Bermuda',
+             16: 'Bosnia and Herzegovina',
+             17: 'Brazil',
+             18: 'Brunei Darussalam',
+             19: 'Bulgaria',
+             20: 'Cabo Verde',
+             21: 'Canada',
+             22: 'Cayman Islands',
+             23: 'Chile',
+             24: 'Colombia',
+             25: 'Costa Rica',
+             26: 'Croatia',
+             27: 'Cyprus',
+             28: 'Czech Republic',
+             29: 'Denmark',
+             30: 'Dominica',
+             31: 'Ecuador',
+             32: 'Egypt',
+             33: 'El Salvador',
+             34: 'Estonia',
+             35: 'Fiji',
+             36: 'Finland',
+             37: 'France',
+             38: 'French Guiana',
+             39: 'Georgia',
+             40: 'Germany',
+             41: 'Greece',
+             42: 'Grenada',
+             43: 'Guadeloupe',
+             44: 'Guatemala',
+             45: 'Guyana',
+             46: 'Hong Kong SAR',
+             47: 'Hungary',
+             48: 'Iceland',
+             49: 'Iran (Islamic Rep of)',
+             50: 'Ireland',
+             51: 'Israel',
+             52: 'Italy',
+             53: 'Jamaica',
+             54: 'Japan',
+             55: 'Kazakhstan',
+             56: 'Kiribati',
+             57: 'Kuwait',
+             58: 'Kyrgyzstan',
+             59: 'Latvia',
+             60: 'Lithuania',
+             61: 'Luxembourg',
+             62: 'Macau',
+             63: 'Maldives',
+             64: 'Malta',
+             65: 'Martinique',
+             66: 'Mauritius',
+             67: 'Mayotte',
+             68: 'Mexico',
+             69: 'Mongolia',
+             70: 'Montenegro',
+             71: 'Netherlands',
+             72: 'New Zealand',
+             73: 'Nicaragua',
+             74: 'Norway',
+             75: 'Oman',
+             76: 'Panama',
+             77: 'Paraguay',
+             78: 'Philippines',
+             79: 'Poland',
+             80: 'Portugal',
+             81: 'Puerto Rico',
+             82: 'Qatar',
+             83: 'Republic of Korea',
+             84: 'Republic of Moldova',
+             85: 'Reunion',
+             86: 'Rodrigues',
+             87: 'Romania',
+             88: 'Russian Federation',
+             89: 'Saint Kitts and Nevis',
+             90: 'Saint Lucia',
+             91: 'Saint Vincent and Grenadines',
+             92: 'San Marino',
+             93: 'Sao Tome and Principe',
+             94: 'Serbia',
+             95: 'Seychelles',
+             96: 'Singapore',
+             97: 'Slovakia',
+             98: 'Slovenia',
+             99: 'South Africa',
+             100: 'Spain',
+             101: 'Sri Lanka',
+             102: 'Suriname',
+             103: 'Sweden',
+             104: 'Switzerland',
+             105: 'TFYR Macedonia',
+             106: 'Thailand',
+             107: 'Trinidad and Tobago',
+             108: 'Turkey',
+             109: 'Turkmenistan',
+             110: 'Ukraine',
+             111: 'United Arab Emirates',
+             112: 'United Kingdom',
+             113: 'United States of America',
+             114: 'Uruguay',
+             115: 'Uzbekistan',
+             116: 'Venezuela (Bolivarian Republic of)',
+             117: 'Virgin Islands (USA)',
+             118: 'Cuba',
+             }

training.py

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+import sys
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+import tensorflow as tf
+from countries_map import countries
+
+
+def mapSet(set):
+    age = {"5-14 years": 0, "15-24 years": 1, "25-34 years": 2,
+           "35-54 years": 3, "55-74 years": 4, "75+ years": 5}
+    sex = {"male": 0, "female": 1}
+
+    set["age"].replace(age, inplace=True)
+    set["sex"].replace(sex, inplace=True)
+    set["country"].replace({v: k for k, v in countries.items()}, inplace=True)
+
+    return set
+
+
+column_names = ["country", "year", "sex", "age", "suicides_no", "population"]
+feature_names = ["country", "year", "sex", "age", "population"]
+label_name = column_names[4]
+
+sc = pd.read_csv('who_suicide_statistics.csv')
+
+train, validate, test = np.split(sc.sample(frac=1, random_state=42),
+                                 [int(.6*len(sc)), int(.8*len(sc))])
+train.dropna(inplace=True)
+validate.dropna(inplace=True)
+test.dropna(inplace=True)
+
+train_n = mapSet(train)
+validate_n = mapSet(validate)
+test_n = mapSet(validate)
+
+train_csv = pd.DataFrame.to_csv(train_n, index=False)
+
+train_dataset = tf.data.experimental.make_csv_dataset(
+    train_csv,
+    1000,
+    column_names=column_names,
+    label_name=label_name,
+    num_epochs=1)
+
+features, labels = next(iter(train_dataset))
+print(features)
+
+plt.scatter(features['year'],
+            features['age'],
+            c=labels,
+            cmap='sex')
+
+plt.xlabel("year")
+plt.ylabel("age")
+plt.show()
+
+print("Features: {}".format(feature_names))
+print("Label: {}".format(label_name))
+
+# print(train)