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fix(data.py): precipication value was same as temp

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This commit is contained in:
Mahdi Dibaiee 2019-02-14 12:36:09 +03:30
parent 4318cf71be
commit a2ff08b195
3 changed files with 61 additions and 10 deletions
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8
data.py
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@ -87,16 +87,16 @@ def get_point_information(longitude, latitude):
autumn_precip = [yp[month] for month in AUTUMN_MONTHS]
item['temp_winter_{}'.format(year)] = np.mean(winter_temp)
item['precip_winter_{}'.format(year)] = np.mean(winter_temp)
item['precip_winter_{}'.format(year)] = np.mean(winter_precip)
item['temp_spring_{}'.format(year)] = np.mean(spring_temp)
item['precip_spring_{}'.format(year)] = np.mean(spring_temp)
item['precip_spring_{}'.format(year)] = np.mean(spring_precip)
item['temp_summer_{}'.format(year)] = np.mean(summer_temp)
item['precip_summer_{}'.format(year)] = np.mean(summer_temp)
item['precip_summer_{}'.format(year)] = np.mean(summer_precip)
item['temp_autumn_{}'.format(year)] = np.mean(autumn_temp)
item['precip_autumn_{}'.format(year)] = np.mean(autumn_temp)
item['precip_autumn_{}'.format(year)] = np.mean(autumn_precip)
return item

9
nn.py
View File

@ -13,12 +13,17 @@ from utils import *
tf.enable_eager_execution()
df = pd.read_pickle('data_distance.p')
df = pd.read_pickle('data_final.p')
# print(df.head())
dataset = dataframe_to_dataset_biomes(df)
# dataset = dataframe_to_dataset_biomes(df)
dataset = dataframe_to_dataset_temp_precip(df)
i = 0
for feature, target in dataset:
i += 1
if i > 10:
break
print('{} => {}'.format(feature, target))
print(tf.__version__)

52
utils.py
View File

@ -6,6 +6,22 @@ from constants import *
inputs = ['elevation', 'distance_to_water']
output = 'biome_num'
def normalize(v):
return (v - np.min(v)) / (np.max(v) - np.min(v))
def normalize_ndarray(ar):
tr = np.transpose(ar)
for i in range(tr.shape[0]):
tr[i] = normalize(tr[i])
return np.transpose(tr)
def normalize_df(df):
for col in df.columns:
df[col] = normalize(df[col])
return df
def dataframe_to_dataset_biomes(df):
rows = df.shape[0]
@ -16,7 +32,6 @@ def dataframe_to_dataset_biomes(df):
tf_inputs = np.empty((0, columns))
tf_output = np.empty((0))
latitude = np.array(df.index.get_level_values(1))
longitude = np.array(df.index.get_level_values(0))
for year in range(MIN_YEAR, MAX_YEAR + 1):
local_inputs = list(inputs)
@ -33,7 +48,38 @@ def dataframe_to_dataset_biomes(df):
tf_inputs = np.concatenate((tf_inputs, local_df.values), axis=0)
tf_output = np.concatenate((tf_output, df[output].values), axis=0)
tf_inputs = tf.cast(tf_inputs, tf.float32)
tf_output = tf.cast(tf_output, tf.int32)
tf_inputs = tf.cast(normalize_ndarray(tf_inputs), tf.float32)
tf_output = tf.cast(normalize_ndarray(tf_output), tf.int32)
return tf.data.Dataset.from_tensor_slices((tf_inputs, tf_output))
def dataframe_to_dataset_temp_precip(df):
rows = df.shape[0]
# elevation, distance_to_water, latitude
# season, year
columns = 5
tf_inputs = np.empty((0, columns))
tf_output = np.empty((0, 2))
latitude = np.array(df.index.get_level_values(1))
for year in range(MIN_YEAR, MAX_YEAR + 1):
local_inputs = list(inputs)
for idx, season in enumerate(SEASONS):
season_index = idx / len(season)
local_df = df[local_inputs]
local_df.loc[:, 'latitude'] = pd.Series(latitude, index=local_df.index)
local_df.loc[:, 'season'] = pd.Series(np.repeat(season_index, rows), index=local_df.index)
local_df.loc[:, 'year'] = pd.Series(np.repeat(year, rows), index=local_df.index)
output = ['temp_{}_{}'.format(season, year), 'precip_{}_{}'.format(season, year)]
tf_inputs = np.concatenate((tf_inputs, local_df.values), axis=0)
tf_output = np.concatenate((tf_output, df[output].values), axis=0)
tf_inputs = tf.cast(normalize_ndarray(tf_inputs), tf.float32)
tf_output = tf.cast(normalize_ndarray(tf_output), tf.float32)
return tf.data.Dataset.from_tensor_slices((tf_inputs, tf_output))