fix(data.py): precipication value was same as temp
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8
data.py
8
data.py
@ -87,16 +87,16 @@ def get_point_information(longitude, latitude):
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autumn_precip = [yp[month] for month in AUTUMN_MONTHS]
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item['temp_winter_{}'.format(year)] = np.mean(winter_temp)
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item['precip_winter_{}'.format(year)] = np.mean(winter_temp)
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item['precip_winter_{}'.format(year)] = np.mean(winter_precip)
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item['temp_spring_{}'.format(year)] = np.mean(spring_temp)
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item['precip_spring_{}'.format(year)] = np.mean(spring_temp)
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item['precip_spring_{}'.format(year)] = np.mean(spring_precip)
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item['temp_summer_{}'.format(year)] = np.mean(summer_temp)
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item['precip_summer_{}'.format(year)] = np.mean(summer_temp)
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item['precip_summer_{}'.format(year)] = np.mean(summer_precip)
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item['temp_autumn_{}'.format(year)] = np.mean(autumn_temp)
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item['precip_autumn_{}'.format(year)] = np.mean(autumn_temp)
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item['precip_autumn_{}'.format(year)] = np.mean(autumn_precip)
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return item
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9
nn.py
9
nn.py
@ -13,12 +13,17 @@ from utils import *
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tf.enable_eager_execution()
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df = pd.read_pickle('data_distance.p')
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df = pd.read_pickle('data_final.p')
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# print(df.head())
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dataset = dataframe_to_dataset_biomes(df)
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# dataset = dataframe_to_dataset_biomes(df)
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dataset = dataframe_to_dataset_temp_precip(df)
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i = 0
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for feature, target in dataset:
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i += 1
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if i > 10:
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break
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print('{} => {}'.format(feature, target))
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print(tf.__version__)
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52
utils.py
52
utils.py
@ -6,6 +6,22 @@ from constants import *
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inputs = ['elevation', 'distance_to_water']
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output = 'biome_num'
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def normalize(v):
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return (v - np.min(v)) / (np.max(v) - np.min(v))
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def normalize_ndarray(ar):
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tr = np.transpose(ar)
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for i in range(tr.shape[0]):
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tr[i] = normalize(tr[i])
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return np.transpose(tr)
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def normalize_df(df):
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for col in df.columns:
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df[col] = normalize(df[col])
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return df
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def dataframe_to_dataset_biomes(df):
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rows = df.shape[0]
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@ -16,7 +32,6 @@ def dataframe_to_dataset_biomes(df):
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tf_inputs = np.empty((0, columns))
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tf_output = np.empty((0))
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latitude = np.array(df.index.get_level_values(1))
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longitude = np.array(df.index.get_level_values(0))
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for year in range(MIN_YEAR, MAX_YEAR + 1):
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local_inputs = list(inputs)
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@ -33,7 +48,38 @@ def dataframe_to_dataset_biomes(df):
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tf_inputs = np.concatenate((tf_inputs, local_df.values), axis=0)
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tf_output = np.concatenate((tf_output, df[output].values), axis=0)
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tf_inputs = tf.cast(tf_inputs, tf.float32)
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tf_output = tf.cast(tf_output, tf.int32)
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tf_inputs = tf.cast(normalize_ndarray(tf_inputs), tf.float32)
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tf_output = tf.cast(normalize_ndarray(tf_output), tf.int32)
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return tf.data.Dataset.from_tensor_slices((tf_inputs, tf_output))
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def dataframe_to_dataset_temp_precip(df):
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rows = df.shape[0]
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# elevation, distance_to_water, latitude
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# season, year
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columns = 5
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tf_inputs = np.empty((0, columns))
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tf_output = np.empty((0, 2))
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latitude = np.array(df.index.get_level_values(1))
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for year in range(MIN_YEAR, MAX_YEAR + 1):
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local_inputs = list(inputs)
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for idx, season in enumerate(SEASONS):
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season_index = idx / len(season)
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local_df = df[local_inputs]
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local_df.loc[:, 'latitude'] = pd.Series(latitude, index=local_df.index)
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local_df.loc[:, 'season'] = pd.Series(np.repeat(season_index, rows), index=local_df.index)
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local_df.loc[:, 'year'] = pd.Series(np.repeat(year, rows), index=local_df.index)
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output = ['temp_{}_{}'.format(season, year), 'precip_{}_{}'.format(season, year)]
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tf_inputs = np.concatenate((tf_inputs, local_df.values), axis=0)
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tf_output = np.concatenate((tf_output, df[output].values), axis=0)
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tf_inputs = tf.cast(normalize_ndarray(tf_inputs), tf.float32)
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tf_output = tf.cast(normalize_ndarray(tf_output), tf.float32)
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return tf.data.Dataset.from_tensor_slices((tf_inputs, tf_output))
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