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Commit 2925c8ff authored by Dumoulin Nicolas's avatar Dumoulin Nicolas
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NEW: new datastructure for storing scenarios

parent 76a0c21b
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scenariosAleatoires/MCMC.py
View file @ 2925c8ff
......@@ -5,106 +5,13 @@ import geopandas as gpd
import os, sys, time, shutil
import yaml
import numpy as np
from Indicator import Indicator
from overrides import overrides
from proximite import Proximite as Proximity
from resilience_list import Resilience
from productivite import Productivity
from indice_biodiversite_2 import Biodiversity
from social import Social
from indicators import Indicators
from tqdm import tqdm
import patutils
from patutils import md5sum, load_pat_patches
import seaborn as sns
import matplotlib.pyplot as plt
import multiprocessing as mp
from functools import partial
import itertools
class Scenario:
_counter = itertools.count()
def __init__(self, patches):
self.patches = patches
self.id = next(Scenario._counter)
def load_shp(shpfilename):
'''
Return an instance of class Patches by loading a shapefile of initial patches.
'''
scenario = Scenario(load_pat_patches(shpfilename))
return scenario
def reallocate(self, rng, targetPAT, ratioNbPatches):
nbPatches = int(len(self.patches)*ratioNbPatches)
surfDelta = targetPAT - self.patches.groupby('cultgeopat')['SURF_PARC'].sum()
cult_to_decrease = surfDelta[surfDelta<0].sort_values(ascending=True).keys().tolist()
cult_to_increase = surfDelta[surfDelta>0].sort_values(ascending=False).keys().tolist()
# Sampling the patches to reallocate
if len(self.patches[self.patches['cultgeopat'].isin(cult_to_decrease)]) >= nbPatches:
samples = self.patches[self.patches['cultgeopat'].isin(cult_to_decrease)].sample(n=nbPatches, random_state=rng)#.reset_index(drop=True)
else:
# All cultural types have decreased to objective
samples = self.patches.sample(n=nbPatches, random_state=rng)
# Building the new culture reallocated
if len(cult_to_increase) > 0:
factors = surfDelta[cult_to_increase]
factors = (factors*len(samples)/factors.sum()).map(round) # normalize on nb samples
newCult = pd.Series(cult_to_increase).repeat(factors)
else:
# All cultural types have increased to objective
# So we used all cultures with more weight for highest delta
factors = -1/surfDelta
factors = (factors*len(samples)/factors.sum()).map(round) # normalize on nb samples
newCult = pd.Series(surfDelta.keys().tolist()).repeat(factors)
if len(newCult) < len(samples): # may be due to factors rounding
newCult = newCult.append(newCult.sample(n=len(samples)-len(newCult), random_state=rng), ignore_index=True)
newCult = newCult.sample(frac=1, random_state=rng)[:len(samples)].reset_index(drop=True) # shuffle and cut extra elements
# Doing the reallocation
self.patches.loc[samples.index.values,'cultgeopat'] = newCult.values
self.patches.loc[samples.index.values,'cultmodified'] = True
class CulturalIndicator(Indicator):
@overrides
def __init__(self, config, initial_patches=None, patches_md5sum=None, targetPAT=None):
self.cultgeopat = config
@overrides
def compute_indicator(self, patches):
return patches[patches['cultgeopat']==self.cultgeopat]['SURF_PARC'].sum()
class TargetDeltaIndicator(Indicator):
'''
This indicator computes the delta between the surfaces of cultural and the given target
'''
@overrides
def __init__(self, config=None, initial_patches=None, patches_md5sum=None, targetPAT=None):
self.targetPAT = targetPAT
@overrides
def compute_indicator(self, patches):
surfDelta = self.targetPAT - patches.groupby('cultgeopat')['SURF_PARC'].sum()
return surfDelta.abs().sum()
class Indicators:
def __init__(self, config, initial_patches, patches_md5sum, targetPAT):
self.indicators_names = ['Proximity', 'Resilience', 'Productivity', 'Biodiversity', 'Social']
self._indicators = {
indicator: eval(indicator)(config.get(indicator.lower()), initial_patches, patches_md5sum, targetPAT)
for indicator in self.indicators_names
}
for cultgeopat in targetPAT.index.tolist():
self._indicators[cultgeopat] = CulturalIndicator(cultgeopat)
self.indicators_names.append(cultgeopat)
self._indicators['TargetDelta'] = TargetDeltaIndicator(targetPAT=targetPAT)
self.indicators_names.append('TargetDelta')
def compute_indicators(self, patches):
return [self._indicators[ind].compute_indicator(patches) for ind in self.indicators_names]
def compute_indicators_pool(self, scenarios):
rows=[]
for patches in scenarios:
rows.append(self.compute_indicators(patches))
return pd.DataFrame(rows, columns=self.indicators_names)
from scenarios import ScenariosStack
class MCMC:
def __init__(self, mcmc_config_filename):
......@@ -113,7 +20,6 @@ class MCMC:
print('Please copy the template file "MCMC_config.sample.yml" and adjust to your settings and run again this program')
sys.exit(1)
self.mcmc_config = yaml.load(open(mcmc_config_filename,'r'))
self.multiprocessing = self.mcmc_config.get('multiprocessing', False)
self.write_data = self.mcmc_config.get('write_data', False)
self.patches_md5sum = md5sum(self.mcmc_config['patches'])
if 'rng_seed' in self.mcmc_config:
......@@ -146,8 +52,8 @@ class MCMC:
yaml.dump(config_data, outfile, default_flow_style=False)
# finishing init
self.patches = load_pat_patches(self.mcmc_config['patches'])
self.patches['cultmodified'] = False
self.target = pd.read_csv(self.mcmc_config['target'], sep=';',index_col=0)
self.surfaces = self.patches['SURF_PARC']
self.target = pd.read_csv(self.mcmc_config['target'], sep=';',index_col=0).rename(index=patutils.code_cultgeopat)
targetRatio = (self.target['2050']-self.target['2016'])/self.target['2016']
self.targetPAT = self.patches.groupby('cultgeopat')['SURF_PARC'].sum()*(1+targetRatio)
self.indicators = Indicators(self.mcmc_config['indicators_config'], self.patches, self.patches_md5sum, self.targetPAT)
......@@ -183,55 +89,30 @@ class MCMC:
def scenario_scores(patches, indicators):
return indicators.compute_indicators(patches)
def step(self, iter_nb, nb_particles, scenarios, scores=None, write_data=False):
def step(self, iter_nb, nb_particles, scenarios, write_data=False):
'''
Sample new scenarios, evaluate their scores and retain only pareto front.
:param iter_nb: (int) number of this iteration, 0 for initial step, 1 for first iteration and so on.
:param iter_nb: (int) number of this iteration.
:param nb_particles: number of new scenarios to sample
:param scenarios: (DataFrame) list of the scenarios used as base for sampling the new scenarios.
:param scores: (DataFrame) list of scores for each scenario. May be None
if given scenario are only used for sampling and will not be considered for pareto front.
:param scenarios: (ScenariosStack) list of the scenarios used as base for sampling the new scenarios.
'''
new_scenarios = []
new_scenarios_id = []
new_scores = []
# Loop of sampling and scoring
for index, patches in tqdm(scenarios.sample(nb_particles, replace=True, random_state=self.rng).iterrows(), total=nb_particles):
scenario = Scenario(patches[0].copy())
scenario.reallocate(self.rng, self.targetPAT, self.mcmc_config['ratio_patches_to_modify']) # 3.8 ms
new_scenarios.append(scenario.patches)
new_scenarios_id.append(scenario.id)
if not self.multiprocessing:
new_scores.append(self.indicators.compute_indicators(scenario.patches))
if self.multiprocessing:
new_scores = list(tqdm(self.pool.imap(partial(MCMC.scenario_scores, indicators=self.indicators), new_scenarios, chunksize=50), total=len(new_scenarios)))
# merging with precedent data
start_time = time.time()
new_scores = pd.DataFrame(new_scores, index=new_scenarios_id, columns=self.indicators.indicators_names)
new_scores['iteration'] = iter_nb
if scores is None:
scores = new_scores
scenarios = pd.DataFrame(new_scenarios, columns=['patches'], index=new_scenarios_id)
scenarios['iteration'] = iter_nb
else:
scores = scores.append(new_scores, sort=False)
new_scenarios = pd.DataFrame(new_scenarios, columns=['patches'], index=new_scenarios_id)
new_scenarios['iteration'] = iter_nb
scenarios = scenarios.append(new_scenarios, sort=False)
elapsed_time = time.time() - start_time
print('Data merged in {} - '.format(time.strftime("%M:%S", time.gmtime(elapsed_time))), end="", flush=True)
for index, scenario in tqdm(scenarios.sample(nb_particles, self.rng).iterrows(), total=nb_particles):
scenario = scenarios.reallocate(index, self.rng, self.patches, self.targetPAT, self.mcmc_config['ratio_patches_to_modify'])
scenarios.append(iter_nb, scenario)
# Computing pareto front
start_time = time.time()
pareto_mask = MCMC.is_pareto_efficient(scores[['Resilience','Proximity','Productivity','Biodiversity','Social','TargetDelta']].values)
scores['pareto'] = pareto_mask
pareto_mask = MCMC.is_pareto_efficient(scenarios.scores[['Resilience','Proximity','Productivity','Biodiversity','Social','TargetDelta']].values)
scenarios.scores['pareto'] = pareto_mask
elapsed_time = time.time() - start_time
print('Pareto front computed in {} - '.format(time.strftime("%M:%S", time.gmtime(elapsed_time))), end="", flush=True)
# Writing output data
start_time = time.time()
scores.to_csv(self.outputdir+'/mcmc_iter_{0:03d}.csv'.format(iter_nb), index=True)
scenarios.scores.to_csv(self.outputdir+'/scores_iter_{0:03d}.csv'.format(iter_nb), index=True)
scenarios.cultgeopat.to_csv(self.outputdir+'/cult_iter_{0:03d}.csv'.format(iter_nb), index=True)
try:
pairplot = sns.pairplot(scores, vars=['Resilience','Proximity','Productivity','Biodiversity','Social','TargetDelta'],
pairplot = sns.pairplot(scenarios.scores, vars=['Resilience','Proximity','Productivity','Biodiversity','Social','TargetDelta'],
diag_kind="kde",hue="pareto"
)
pairplot.savefig(self.outputdir+'/mcmc_iter_{0:03d}.png'.format(iter_nb))
......@@ -241,47 +122,39 @@ class MCMC:
# and an error is triggered. Here, we ignore this error.
pass
elapsed_time = time.time() - start_time
print('Scores written in {} - '.format(time.strftime("%M:%S", time.gmtime(elapsed_time))), end="", flush=True)
print('Scores written in {} '.format(time.strftime("%M:%S", time.gmtime(elapsed_time))), end="", flush=True)
# Retaining only optimal particules
scenarios = scenarios[pareto_mask]
scores = scores[pareto_mask]
scenarios.retain(pareto_mask)
if write_data:
# Writing shapefiles
start_time = time.time()
shp_dir = self.outputdir + '/patches_iter_{0:03d}'.format(iter_nb)
os.makedirs(shp_dir)
for index,scenario in scenarios.iterrows():
scenario = scenario[0]
scenario[scenario['cultmodified']].drop('cultmodified', axis=1).to_file(shp_dir+'/{0:03d}_{}'.format(iter_nb,index), encoding='utf-8')
for index,scenario in scenarios.cultgeopat.iterrows():
concat = pd.concat([self.patches, scenario.to_frame("newcult")], axis=1)
concat = concat[concat['init_cult']!=concat['newcult']]
if len(concat)>0:
for c in ['newcult','cultgeopat','init_cult']:
patutils.decode_cultgeopat(concat, c)
concat.to_file(shp_dir+'/{:03d}_{}'.format(iter_nb, index), encoding='utf-8')
# scenario[scenario['cultmodified']].drop('cultmodified', axis=1).to_file(shp_dir+'/{0:03d}_{}'.format(iter_nb,index), encoding='utf-8')
elapsed_time = time.time() - start_time
print(' - Shape files written in {}'.format(time.strftime("%M:%S", time.gmtime(elapsed_time))), end="", flush=True)
print()
return [scenarios, scores]
return scenarios
def run(self, nb_processes=mp.cpu_count()):
if self.multiprocessing:
self.pool = mp.Pool(processes=nb_processes)
def run(self):
# Initial sampling and evaluation
nb_particles = self.mcmc_config['initial_nb_particles']
scenarios_init = pd.DataFrame([self.patches])
scenarios, scores = self.step(0, nb_particles, scenarios_init)
# Start with initial scenario
scenarios = ScenariosStack(self.indicators, self.patches)
# Iteration
for i in range(self.mcmc_config['max_iterations']):
print('Iteration #{}'.format(i+1))
# Write SHP only for the last iteration
scenarios, scores = self.step(i+1, nb_particles, scenarios, scores, self.write_data and i == self.mcmc_config['max_iterations']-1)
# TODO
# Storing variation of indicators
init_var = scores.std()
# sequential optimization loop
if self.multiprocessing:
self.pool.close()
return [scenarios,scores]
scenarios = self.step(i, nb_particles, scenarios, self.write_data and i == self.mcmc_config['max_iterations']-1)
return scenarios
if __name__ == '__main__':
mcmc = MCMC('MCMC_config.yml')
# scenario = Scenario(mcmc.patches.copy())
# scenario.reallocate(mcmc.rng, mcmc.targetPAT, mcmc.mcmc_config['ratio_patches_to_modify'])
scenario,scores = mcmc.run()
# print(mcmc.indicators.biodiversity(mcmc.patches))
# print(mcmc.indicators.proximity(mcmc.patches))
scenario = mcmc.run()
scenariosAleatoires/indicators.py 0 → 100644
View file @ 2925c8ff
#!/usr/bin/python
# -*- coding: utf-8 -*-
from overrides import overrides
from Indicator import Indicator
from proximite import Proximite as Proximity
from resilience_list import Resilience
from productivite import Productivity
from indice_biodiversite_2 import Biodiversity
from social import Social
import patutils
class CulturalIndicator(Indicator):
@overrides
def __init__(self, config, initial_patches=None, patches_md5sum=None, targetPAT=None):
self.cultgeopat = patutils.code_cultgeopat[config]
@overrides
def compute_indicator(self, patches):
return patches[patches['cultgeopat']==self.cultgeopat]['SURF_PARC'].sum()
class TargetDeltaIndicator(Indicator):
'''
This indicator computes the delta between the surfaces of cultural and the given target
'''
@overrides
def __init__(self, config=None, initial_patches=None, patches_md5sum=None, targetPAT=None):
self.targetPAT = targetPAT
@overrides
def compute_indicator(self, patches):
surfDelta = self.targetPAT - patches.groupby('cultgeopat')['SURF_PARC'].sum()
return surfDelta.abs().sum()
class Indicators:
def __init__(self, config, initial_patches, patches_md5sum, targetPAT):
self.indicators_names = ['Proximity', 'Resilience', 'Productivity', 'Biodiversity', 'Social']
self._indicators = {
indicator: eval(indicator)(config.get(indicator.lower()), initial_patches, patches_md5sum, targetPAT)
for indicator in self.indicators_names
}
for cultgeopat in sorted(patutils.code_cultgeopat.keys()):
self._indicators[cultgeopat] = CulturalIndicator(cultgeopat)
self.indicators_names.append(cultgeopat)
self._indicators['TargetDelta'] = TargetDeltaIndicator(targetPAT=targetPAT)
self.indicators_names.append('TargetDelta')
def compute_indicators(self, patches):
return [self._indicators[ind].compute_indicator(patches) for ind in self.indicators_names]
scenariosAleatoires/indice_biodiversite_2.py
View file @ 2925c8ff
......@@ -8,6 +8,7 @@ import os
from tqdm import tqdm
from Indicator import Indicator
from overrides import overrides
import patutils
class Biodiversity(Indicator):
'''
......@@ -86,7 +87,7 @@ class Biodiversity(Indicator):
:param patches: List of PAT's patches and their data
'''
meadowmask = patches["cultgeopat"]=='Prairies'
meadowmask = patches["cultgeopat"]==patutils.code_cultgeopat['Prairies']
biodiversity = ((self.probabilities*meadowmask).T*meadowmask).sum()
# We multiply by 100000 to get an indices with a value easy to understand for the users
return self.final_result_ratio/biodiversity
......@@ -122,6 +123,6 @@ if __name__ == '__main__':
from patutils import load_pat_patches, md5sum
patches_filename = "../output/PAT_patches/PAT_patches.shp"
patches = load_pat_patches(patches_filename)
matrix_filename='../output/matrix_biodiversity_test.npz'
matrix_filename='../output/matrix_biodiversity.npz'
biodiv = Biodiversity({'dmax': 1000, 'epsilon': 0.001, 'matrixfilename': matrix_filename}, patches, md5sum(patches_filename))
print(biodiv.compute_indicator(patches))
scenariosAleatoires/patutils.py
View file @ 2925c8ff
......@@ -4,15 +4,36 @@
import hashlib
import geopandas as gpd
def load_pat_patches(filename):
def load_pat_patches(filename, encode_codecult=True):
'''
Load shapefile containing cultural patches and applying initial filters.
'''
patches = gpd.GeoDataFrame.from_file(filename, encoding='utf-8')
for k in ['ID_PARCEL', 'INSEE_COM', 'CODE_EPCI', 'POPULATION','id_ilot','id_expl']:
patches[k] = patches[k].astype('int32')
patches.set_index('ID_PARCEL', inplace=True)
patches = patches[patches['cultgeopat']!='Non Considérée']
patches['init_cult'] = patches['cultgeopat']
if encode_codecult:
encode_cultgeopat(patches)
return patches
code_cultgeopat = {'Cultures industrielles': 0,
'Céréales': 1,
'Fourrages': 2,
'Fruits et légumes': 3,
'Oléagineux': 4,
'Prairies': 5,
'Protéagineux': 6}
reverse_code_cultgeopat = {v: k for k, v in code_cultgeopat.items()}
def encode_cultgeopat(patches):
patches['cultgeopat'].replace(code_cultgeopat, inplace=True)
patches['cultgeopat'] = patches['cultgeopat'].astype('int8')
patches['init_cult'] = patches['cultgeopat']
def decode_cultgeopat(patches, column):
patches[column].replace(reverse_code_cultgeopat, inplace=True)
def md5sum(filename):
hash_md5 = hashlib.md5()
with open(filename, "rb") as f:
......
scenariosAleatoires/productivite.py
View file @ 2925c8ff
......@@ -4,6 +4,7 @@ import geopandas as gpd
import pandas as pd
from Indicator import Indicator
from overrides import overrides
import patutils
class Productivity(Indicator):
'''
......@@ -18,7 +19,7 @@ class Productivity(Indicator):
@overrides
def compute_indicator(self, layer_scenario):
valeurs_cad = layer_scenario[layer_scenario['cultgeopat']=='Fruits et légumes']
valeurs_cad = layer_scenario[layer_scenario['cultgeopat']==patutils.code_cultgeopat['Fruits et légumes']]
valeurs_cad = valeurs_cad['VALEUR_CAD'] / valeurs_cad['SURF_PARC']
return len(valeurs_cad) / valeurs_cad.sum()
......
scenariosAleatoires/proximite.py
View file @ 2925c8ff
......@@ -2,6 +2,7 @@
# -*- coding: utf-8 -*-
from Indicator import Indicator
from overrides import overrides
import patutils
class Proximite(Indicator):
'''
......@@ -18,7 +19,7 @@ class Proximite(Indicator):
popByBdv = initial_patches.groupby('Bdv').apply(lambda x:x.groupby('INSEE_COM')['POPULATION'].first().sum())
# OK but slower
# popByBdv = patches.groupby(['Bdv','INSEE_COM']).first().groupby('Bdv')['POPULATION'].sum()
self.targetSurfByBdv = targetPAT['Fruits et légumes'] * popByBdv/Population_PAT
self.targetSurfByBdv = targetPAT[patutils.code_cultgeopat['Fruits et légumes']] * popByBdv/Population_PAT
@overrides
def compute_indicator(self, patches):
......@@ -28,7 +29,7 @@ class Proximite(Indicator):
:param affichage: True if we want the display in the console, False is the other case
'''
# get area of "fruits et légumes" in the scenario
flSurfByBdv = patches[patches['cultgeopat']=='Fruits et légumes'].groupby('Bdv').agg({'SURF_PARC':sum})
flSurfByBdv = patches[patches['cultgeopat']==patutils.code_cultgeopat['Fruits et légumes']].groupby('Bdv').agg({'SURF_PARC':sum})
result = flSurfByBdv.div(self.targetSurfByBdv,axis=0).fillna(0)
if result.where(result<1).count().sum() == 0:
# All Bdv are fullfilled
......@@ -44,7 +45,7 @@ if __name__ == '__main__':
from patutils import load_pat_patches
patches_filename = "../output/PAT_patches/PAT_patches.shp"
patches = load_pat_patches(patches_filename)
target = pd.read_csv('../resources/targetPAT.csv', sep=';',index_col=0)
target = pd.read_csv('../resources/targetPAT.csv', sep=';',index_col=0).rename(index=patutils.code_cultgeopat)
targetRatio = (target['2050']-target['2016'])/target['2016']
targetPAT = patches.groupby('cultgeopat')['SURF_PARC'].sum()*(1+targetRatio)
prox = Proximite(None, patches, None, targetPAT)
......
scenariosAleatoires/resilience_list.py
View file @ 2925c8ff
......@@ -19,8 +19,9 @@ class Resilience(Indicator):
# caching intersection between patches and grid
self.intersection = gpd.sjoin(
self.grid[['id','geometry']],
initial_patches[['geometry','SURF_PARC','ID_PARCEL']],
initial_patches[['geometry','SURF_PARC']].copy(),
how='inner', op='intersects')
self.intersection.rename(columns={'index_right':'ID_PARCEL'}, inplace=True)
# caching surface of patches in each cell
self.intersection['surf_cell'] = self.intersection[['id','SURF_PARC']].groupby('id').transform(sum)
self.size = len(self.intersection.groupby('id'))
......@@ -42,14 +43,14 @@ class Resilience(Indicator):
:param patches: The scenario to analyse as a list
'''
# merging given patches with table of intersection patches/grid
intersection2 = pd.merge(self.intersection, patches[['ID_PARCEL','cultgeopat','SURF_PARC']],on='ID_PARCEL',how='left')
intersection2 = pd.merge(self.intersection, patches[['cultgeopat','SURF_PARC']],left_on='ID_PARCEL', right_index=True,how='left')
# building pivot table on grid cell and cultgeopat
pivot = pd.pivot_table(intersection2,values=['SURF_PARC_x','surf_cell'],index=['id','cultgeopat'],aggfunc={'SURF_PARC_x':np.sum,'surf_cell':'first'})
pivot['res'] = pivot['SURF_PARC_x']/pivot['surf_cell']
return - self.size / (pivot['res'] * pivot['res'].apply(math.log2)).sum()
if __name__ == '__main__':
from patutils import load_pat_patches
from patutils import load_pat_patches, code_cultgeopat
patches_filename = "../output/PAT_patches/PAT_patches.shp"
patches = load_pat_patches(patches_filename)
import time
......@@ -58,3 +59,7 @@ if __name__ == '__main__':
print(res.compute_indicator(patches))
elapsed = time.time()-start
print(elapsed)
cult_col_index = patches.columns.get_loc('cultgeopat')
for i in range(5000):
patches.iat[i,cult_col_index]=code_cultgeopat['Céréales']
print(res.compute_indicator(patches))
scenariosAleatoires/scenarios.py 0 → 100644
View file @ 2925c8ff
#!/usr/bin/python
# -*- coding: utf-8 -*-
import pandas as pd
import itertools
from patutils import load_pat_patches
class ScenariosStack:
def __init__(self, indicators, initial_patches):
self._counter = itertools.count()
scenario_id = next(self._counter)
self.indicators = indicators
self.cultgeopat = initial_patches[['cultgeopat']]
self.cultgeopat = self.cultgeopat.T
# Now we have the id_parcel as columns, and each row will represent a scenario
self.cultgeopat.rename(index={'cultgeopat':scenario_id}, inplace=True)
# self.cultmodified = pd.DataFrame([[False]*len(initial_patches)], columns=initial_patches.index.values, dtype=bool)
self.scores = pd.DataFrame([self.indicators.compute_indicators(initial_patches) + [0, False]], columns=self.indicators.indicators_names+['iteration','pareto'], dtype='float64')
self.scores['iteration'] = self.scores['iteration'].astype('int')
def append(self, nb_iter, patches_cult):
id = next(self._counter)
self.cultgeopat.loc[id] = patches_cult['cultgeopat'].values
# self.cultmodified[id] = False
self.scores.at[id] = self.indicators.compute_indicators(patches_cult) + [nb_iter, False]
self.scores['iteration'] = self.scores['iteration'].astype('int')
def retain(self, mask):
self.cultgeopat = self.cultgeopat[mask]
# self.cultmodified = self.cultmodified[mask]
self.scores = self.scores[mask]
def drop_scenario(self, id_scenario):
pass
def sample(self, nb, rng):
return self.cultgeopat.sample(nb, replace=True, random_state=rng)
def consolidate_scenario(self, id, patches, columns):
selected_data = patches[columns]
return pd.concat([self.cultgeopat.loc[id].to_frame("cultgeopat"), selected_data],axis=1)
def reallocate(self, scen_id, rng, initial_patches, targetPAT, ratioNbPatches):
patches = self.consolidate_scenario(scen_id, initial_patches, ['SURF_PARC', 'Bdv','init_cult', 'VALEUR_CAD'])
nbPatches = int(len(patches)*ratioNbPatches)
surfDelta = targetPAT - patches.groupby('cultgeopat')['SURF_PARC'].sum()
cult_to_decrease = surfDelta[surfDelta<0].sort_values(ascending=True).keys().tolist()
cult_to_increase = surfDelta[surfDelta>0].sort_values(ascending=False).keys().tolist()
targetReached = False
# Sampling the patches to reallocate
patches_to_decrease = patches[patches['cultgeopat'].isin(cult_to_decrease)]
samples = patches_to_decrease.sample(n=min(nbPatches,len(patches_to_decrease)), random_state=rng)#.reset_index(drop=True)
# Building the new culture reallocated
if len(cult_to_increase) > 0:
factors = surfDelta[cult_to_increase]
factors = (factors*len(samples)/factors.sum()).map(round) # normalize on nb samples
newCult = pd.Series(cult_to_increase).repeat(factors)
else:
# All cultural types have increased to objective
# So we used all cultures with more weight for highest delta
raise RuntimeError("Reallocation impossible: All cultural types have reached the target.")
if len(newCult) < len(samples): # may be due to factors rounding
newCult = newCult.append(newCult.sample(n=len(samples)-len(newCult), random_state=rng), ignore_index=True)
newCult = newCult.sample(frac=1, random_state=rng)[:len(samples)].reset_index(drop=True) # shuffle and cut extra elements
# Doing the reallocation
patches.loc[samples.index.values,'cultgeopat'] = newCult.values
return patches
scenariosAleatoires/social.py
View file @ 2925c8ff
......@@ -3,6 +3,7 @@
import pandas as pd
import numpy as np
import os
import patutils
from patutils import md5sum
from Indicator import Indicator
from overrides import overrides
......@@ -27,6 +28,8 @@ class Social(Indicator):
'''
self.patches_md5sum = patches_md5sum
self.matrice_transition = pd.read_csv(social_config['cost_matrix_filename'], sep='\t',index_col=0)
self.matrice_transition.rename(index=patutils.code_cultgeopat, inplace=True)
self.matrice_transition.rename(columns=patutils.code_cultgeopat, inplace=True)
self.cost_matrix_md5sum = md5sum(social_config['cost_matrix_filename'])
# proportion of a cultural type needed for considering that this cultural type
# is suffisantly present in the locality for ignoring the conversion cost.
......@@ -37,6 +40,12 @@ class Social(Indicator):
self.save_patches_transition_cost(costs_filename)
else:
self.load_patches_transition_cost(costs_filename)
try:
self.patches_transition_cost.replace({'initialcult':patutils.code_cultgeopat,'cultgeopat':patutils.code_cultgeopat},inplace=True)
except e:
pass
self.patches_transition_cost.set_index('ID_PARCEL', inplace=True)
self.patches_transition_cost.index = self.patches_transition_cost.index.astype('int')
def compute_patches_transition_cost(self, initial_patches):
costs=[]
......@@ -52,7 +61,7 @@ class Social(Indicator):
# patches that have different farming that others of the same farmer
if culture not in initial_patches[initial_patches['id_expl']==row.id_expl]['cultgeopat']:
cost = self.matrice_transition.loc[row.cultgeopat,culture]
costs.append([row.ID_PARCEL, row.cultgeopat, culture, cost])
costs.append([row.Index, row.cultgeopat, culture, cost])
self.patches_transition_cost = pd.DataFrame(costs, columns=['ID_PARCEL','initialcult','cultgeopat','cost'])
def save_patches_transition_cost(self, filename):
......@@ -85,13 +94,13 @@ class Social(Indicator):
:param scenario: The scenario to analyse
'''
cost = 0
patches_altered = scenario_patches[scenario_patches['cultgeopat'] != scenario_patches['init_cult']]
cost = pd.merge(patches_altered, self.patches_transition_cost,on=['ID_PARCEL','cultgeopat'], how='left')['cost'].sum()
patches_altered = scenario_patches[scenario_patches['cultgeopat'] != scenario_patches['init_cult']]['cultgeopat']
cost += pd.merge(patches_altered, self.patches_transition_cost, on=['ID_PARCEL','cultgeopat'], how='left')['cost'].sum()
return cost
if __name__ ==