## #State of the art...in REPLACE # 75/25 XGB_ANS 0.527962384577 #latitude values are primarily between 40.6 and 40.9 #longitude values range between -73.8 and -74.02 #================================================ import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) from scipy import sparse from sklearn.ensemble import RandomForestClassifier from sklearn import model_selection, preprocessing, ensemble from sklearn.metrics import log_loss import xgboost as xgb from sklearn import preprocessing, ensemble from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer #from subprocess import check_output #print(check_output(["ls", "../input"]).decode("utf8")) from sklearn import model_selection, preprocessing, ensemble pd.set_option('display.max_columns', None) df_train = pd.read_json(open("../input/train.json", "r")) target_num_map = {'high':0, 'medium':1, 'low':2} yy = np.array(df_train['interest_level'].apply(lambda x: target_num_map[x])) yxa = df_train['interest_level'] df_test = pd.read_json(open("../input/test.json", "r")) llista=np.array(df_test["listing_id"]) print(df_train.shape) print(df_test.shape) #Outliars... df_train.loc[df_train['price'] == 111111, 'price'] = 1025 #COLUMNS.... #'bathrooms', 'bedrooms', 'building_id', 'created', 'description', # 'display_address', 'features', 'interest_level', 'latitude', # 'listing_id', 'longitude', 'manager_id', 'photos', 'price', # 'street_address'] #======================================== ##Block experiment... #df100= (df_train[["block","interest_level"]]) #interest_dummies = pd.get_dummies(df_train.interest_level) #df100 = pd.concat([df100,interest_dummies[['low','medium','high']]], axis = 1).drop('interest_level', axis = 1) #print(df100.head()) #gby = pd.concat([df100.groupby('block').mean(),df100.groupby('block').count()], axis = 1).iloc[:,:-2] #gby.columns = ['low','medium','high','count'] #print(gby.sort_values(by = 'count', ascending = False).head()) ##End of experiment #====================================== df_train["listing_id"] = df_train["listing_id"] - 68119576.0 df_train["price"]=np.log(df_train["price"]) df_train["zero_building_id"] = df_train["building_id"].apply(lambda x: 1 if x=='0' else 0) df_train["num_photos"] = df_train["photos"].apply(len) df_train["num_features"] = df_train["features"].apply(len) df_train["num_description_words"] = df_train["description"].apply(lambda x: len(x.split(" "))) df_train["created"] = pd.to_datetime(df_train["created"]) df_train["created_year"] = df_train["created"].dt.year df_train["created_month"] = df_train["created"].dt.month df_train["created_day"] = df_train["created"].dt.day df_train["created_hour"] = df_train["created"].dt.hour df_train["total_days"] = (df_train["created_month"] -4.0)*30 + df_train["created_day"] + df_train["created_hour"] /25.0 df_train["diff_rank"]= df_train["total_days"]/df_train["listing_id"] df_test["listing_id"] = df_test["listing_id"] - 68119576.0 df_test["price"]=np.log(df_test["price"]) df_test["zero_building_id"] = df_test["building_id"].apply(lambda x: 1 if x=='0' else 0) df_test["num_photos"] = df_test["photos"].apply(len) df_test["num_features"] = df_test["features"].apply(len) df_test["num_description_words"] = df_test["description"].apply(lambda x: len(x.split(" "))) df_test["created"] = pd.to_datetime(df_test["created"]) df_test["created_year"] = df_test["created"].dt.year df_test["created_month"] = df_test["created"].dt.month df_test["created_day"] = df_test["created"].dt.day df_test["created_hour"] = df_test["created"].dt.hour df_test["total_days"] =(df_test["created_month"] -4.0)*30 + df_test["created_day"] + df_test["created_hour"] /25.0 df_test["diff_rank"]= df_test["total_days"]/df_test["listing_id"] #Price per ... df_train["price_bed"] = df_train["price"]/df_train["bedrooms"] df_train["price_bath"] = df_train["price"]/df_train["bathrooms"] df_train["price_bath_bed"] = df_train["price"]/(df_train["bathrooms"] + df_train["bedrooms"]) #*** df_train["bedPerbath"] = (df_train["bedrooms"]/df_train["bathrooms"]) df_train["bedDiffbath"] = (df_train["bedrooms"]-df_train["bathrooms"]) df_train["bedPlusbath"] = (df_train["bedrooms"]+df_train["bathrooms"]) df_train["bedPerc"] = (df_train["bedrooms"]/(df_train["bathrooms"]+df_train["bedrooms"])) #*** df_train.fillna(-1,inplace=True) df_test["price_bed"] = df_test["price"]/df_test["bedrooms"] df_test["price_bath"] = df_test["price"]/df_test["bathrooms"] df_test["price_bath_bed"] = df_test["price"]/(df_test["bathrooms"] + df_test["bedrooms"]) #*** df_test["bedPerbath"] = (df_test["bedrooms"]/df_test["bathrooms"]) df_test["bedDiffbath"] = (df_test["bedrooms"]-df_test["bathrooms"]) df_test["bedPlusbath"] = (df_test["bedrooms"]+df_test["bathrooms"]) df_test["bedPerc"] = (df_test["bedrooms"]/(df_test["bathrooms"]+df_test["bedrooms"])) #*** df_test.fillna(-1,inplace=True) #numfeats num_feats = ["bathrooms", "bedrooms", "latitude", "longitude", "price", "num_photos", "num_features", "num_description_words", "created_year", "created_month", "created_day","created_hour", "price_bed","price_bath","price_bath_bed","zero_building_id", "bedPerbath","bedDiffbath","bedPlusbath","bedPerc","listing_id" ,"diff_rank","total_days" ] #---------------------------------------------------------- #address1 df_train['address1'] = df_train['display_address'] df_test['address1'] = df_test['display_address'] df_train['address1'] = df_train['address1'].apply(lambda x: str(x).lower()) df_test['address1'] = df_test['address1'].apply(lambda x: str(x).lower()) address_map = { 'w': 'west', 'st.': 'street', 'ave': 'avenue', 'st': 'street', 'e': 'east', 'n': 'north', 's': 'south', 'blvd': 'blvd', 'square': 'square', 'park': 'park', 'place': 'place' } def address_map_func(s): s = s.split(' ') out = [] for x in s: if x in address_map: out.append(address_map[x]) else: out.append(x) return ' '.join(out) def dict_init(aa): gdict={} for rr in range(len(aa)): rr1=aa[rr].split(' ') for jj in range(len(rr1)): ww=rr1[jj] z1=gdict.get(ww,-999) if z1==-999: gdict[ww]=1 else: gdict[ww]+=1 return gdict #train['address1'] = train['address1'].apply(lambda x: x.translate(remove_punct_map)) #test['address1'] = test['address1'].apply(lambda x: x.translate(remove_punct_map)) df_train['address1'] = df_train['address1'].apply(lambda x: address_map_func(x)) df_test['address1'] = df_test['address1'].apply(lambda x: address_map_func(x)) new_cols = ['street', 'avenue', 'east', 'west', 'north', 'south','ave','blvd','square','park','place'] for col in new_cols: df_train[col] = df_train['address1'].apply(lambda x: 1 if col in x else 0) df_test[col] = df_test['address1'].apply(lambda x: 1 if col in x else 0) num_feats.append(col) #--------------------------------------------------------- #LabelEncoding categorical = ["display_address", "manager_id", "building_id", "street_address"] for f in categorical: if df_train[f].dtype=='object': #print(f) lbl = preprocessing.LabelEncoder() lbl.fit(list(df_train[f].values) + list(df_test[f].values)) df_train[f] = lbl.transform(list(df_train[f].values)) df_test[f] = lbl.transform(list(df_test[f].values)) num_feats.append(f) #============================================================================= # compute fractions and count for each manager temp = pd.concat([df_train.manager_id,pd.get_dummies(yxa)], axis = 1).groupby('manager_id').mean() temp.columns = ['high_frac','low_frac', 'medium_frac'] temp['count'] = df_train.groupby('manager_id').count().iloc[:,1] temp['one_manager'] = np.where(temp['count']==1, 1, 0) # compute skill temp['manager_skill'] = temp['high_frac']*2 + temp['medium_frac'] # get ixes for unranked managers... unranked_managers_ixes = temp['count']<20 # ... and ranked ones ranked_managers_ixes = ~unranked_managers_ixes # compute mean values from ranked managers and assign them to unranked ones mean_values = temp.loc[ranked_managers_ixes, ['high_frac','low_frac', 'medium_frac','manager_skill']].mean() print(mean_values) temp.loc[unranked_managers_ixes,['high_frac','low_frac', 'medium_frac','manager_skill']] = mean_values.values temp.loc[unranked_managers_ixes,['one_manager']] =0 print(temp.tail(10)) # inner join to assign manager features to the managers in the training dataframe df_train = df_train.merge(temp.reset_index(),how='left', left_on='manager_id', right_on='manager_id') # add the features computed on the training dataset to the validation dataset df_test = df_test.merge(temp.reset_index(),how='left', left_on='manager_id', right_on='manager_id') new_manager_ixes = df_test['high_frac'].isnull() df_test.loc[new_manager_ixes,['high_frac','low_frac', 'medium_frac','manager_skill']] = mean_values.values #============================================================================= #Split - vectorizer # [Cats Allowed, No Fee]--> [Cats_Allowed No_Fee] df_train['features'] = df_train["features"].apply(lambda x: " ".join(["_".join(i.split(" ")).lower() for i in x])) df_test['features'] = df_test["features"].apply(lambda x: " ".join(["_".join(i.split(" ")).lower() for i in x])) #============================================ #Description featuring... lista=["manhattan","central park","subway","train","bikeway","columbus circle"] sfe_train = np.array(df_train['description']) sfe_test = np.array(df_test['description']) for ww in lista: sar_train=np.zeros(df_train.shape[0]) sar_test=np.zeros(df_test.shape[0]) for rr in range(df_train.shape[0]): if ww in sfe_train[rr].lower(): sar_train[rr]=1 else: sar_train[rr]=0 df_train[ww]=sar_train for rr in range(df_test.shape[0]): if ww in sfe_test[rr].lower(): sar_test[rr]=1 else: sar_test[rr]=0 df_test[ww]=sar_test #============================================================================== # Replace-Join features Section... def rep_join(aa,s_from,s_to): aa_new=aa.copy() for rr in range(len(aa)): rr1=aa[rr].split(' ') axa='' for jj in range(len(rr1)): # if rr1[jj]==s_from and len(rr1[jj])!= len(s_from): # print ('NO BINGO...') if rr1[jj]==s_from and len(rr1[jj])== len(s_from): #if s_from=='washer': #print ('Bingo...',rr1[jj]) rr1[jj]=s_to axa+=rr1[jj] axa+=' ' aa_new[rr]=axa return aa_new #================================================================== #TRAIN ****************** ##Direct replace df_train['features']=df_train['features'].str.replace("garden/patio","garden") df_train['features']=df_train['features'].str.replace("patio","garden") df_train['features']=df_train['features'].str.replace("residents_garden","garden") df_train['features']=df_train['features'].str.replace("common_garden","garden") df_train['features']=df_train['features'].str.replace("wifi_access","wifi") df_train['features']=df_train['features'].str.replace("24/7","24") df_train['features']=df_train['features'].str.replace("24-hour","24") df_train['features']=df_train['features'].str.replace("24hr","24") df_train['features']=df_train['features'].str.replace("concierge","doorman") df_train['features']=df_train['features'].str.replace("ft_doorman","doorman") df_train['features']=df_train['features'].str.replace("24_doorman","doorman") df_train['features']=df_train['features'].str.replace("24_hr_doorman","doorman") df_train['features']=df_train['features'].str.replace("doorman_service","doorman") df_train['features']=df_train['features'].str.replace("full-time_doorman","doorman") df_train['features']=df_train['features'].str.replace("gym/fitness","fitness") df_train['features']=df_train['features'].str.replace("fitness_room","fitness") df_train['features']=df_train['features'].str.replace("washer","laundry") df_train['features']=df_train['features'].str.replace("laundry_in_bldg","laundry") df_train['features']=df_train['features'].str.replace("laundry_in_building","laundry") df_train['features']=df_train['features'].str.replace("laundry_in_building/dryer","laundry") df_train['features']=df_train['features'].str.replace("laundry_in_building_&_dryer","laundry") df_train['features']=df_train['features'].str.replace("laundry_room","laundry") df_train['features']=df_train['features'].str.replace("laundry_&_housekeeping","laundry") df_train['features']=df_train['features'].str.replace("laundry_in_unit","laundry") df_train['features']=df_train['features'].str.replace("laundry_in-unit","laundry") df_train['features']=df_train['features'].str.replace("laundry_on_every_floor","laundry") df_train['features']=df_train['features'].str.replace("laundry_on_floor","laundry") df_train['features']=df_train['features'].str.replace("in-unit_laundry/dryer","laundry") df_train['features']=df_train['features'].str.replace("on-site_laundry","laundry") df_train['features']=df_train['features'].str.replace("laundry/dryer","laundry") df_train['features']=df_train['features'].str.replace("high-speed_internet","high_speed_internet") df_train['features']=df_train['features'].str.replace("high_speed_internet_available","high_speed_internet") df_train['features']=df_train['features'].str.replace("parking_available","parking") df_train['features']=df_train['features'].str.replace("parking_space","parking") df_train['features']=df_train['features'].str.replace("on-site_garage","parking") df_train['features']=df_train['features'].str.replace("on-site_parking","parking") df_train['features']=df_train['features'].str.replace("on-site_parking_lot","parking") df_train['features']=df_train['features'].str.replace("full_service_garage","parking") df_train['features']=df_train['features'].str.replace("common_parking/garage","parking") df_train['features']=df_train['features'].str.replace("garage","parking") df_train['features']=df_train['features'].str.replace("assigned-parking-space","private_parking") df_train['features']=df_train['features'].str.replace("storage_available","storage") df_train['features']=df_train['features'].str.replace("storage_facilities_available","storage") df_train['features']=df_train['features'].str.replace("storage_space","storage") df_train['features']=df_train['features'].str.replace("storage_room","storage") df_train['features']=df_train['features'].str.replace("common_storage","storage") df_train['features']=df_train['features'].str.replace("central_a/c","central_air") df_train['features']=df_train['features'].str.replace("central_ac","central_air") df_train['features']=df_train['features'].str.replace("air_conditioning","central_air") df_train['features']=df_train['features'].str.replace("close_to_subway","subway") df_train['features']=df_train['features'].str.replace("roofdeck","roof_deck") df_train['features']=df_train['features'].str.replace("roof-deck","roof_deck") df_train['features']=df_train['features'].str.replace("rooftop_terrace","roof_deck") df_train['features']=df_train['features'].str.replace("rooftop_deck","roof_deck") df_train['features']=df_train['features'].str.replace("roof_access","roof_deck") df_train['features']=df_train['features'].str.replace("common_roof_deck","roof_deck") df_train['features']=df_train['features'].str.replace("swimming_pool","pool") df_train['features']=df_train['features'].str.replace("indoor_pool","pool") df_train['features']=df_train['features'].str.replace("deco_fireplace","fireplaces") df_train['features']=df_train['features'].str.replace("decorative_fireplace","fireplaces") #Word replace (_MMM_) mel_count=df_train['features'].str.contains("washer").sum() print ('BEFORE Replace-Join Section...*************',mel_count) aa=np.array(df_train['features']) df_train['features']=rep_join(aa,"gym_in_building","fitness") aa=np.array(df_train['features']) df_train['features']=rep_join(aa,"gym","fitness") aa=np.array(df_train['features']) df_train['features']=rep_join(aa,"health_club","fitness") aa=np.array(df_train['features']) df_train['features']=rep_join(aa,"fitness_center","fitness") aa=np.array(df_train['features']) df_train['features']=rep_join(aa,"pets_ok","pets_allowed") aa=np.array(df_train['features']) df_train['features']=rep_join(aa,"lounge_room","residents_lounge") aa=np.array(df_train['features']) df_train['features']=rep_join(aa,"wheelchair_ramp","wheelchair_access") aa=np.array(df_train['features']) #New voc setup f_dict=dict_init(aa) dlista= (sorted(f_dict.items(), key=lambda x:x[1])) new_voc=[] for rr in range(-1,-320,-1): if dlista[rr][0]!='XXXXXX' and dlista[rr][0]!='': new_voc.append(dlista[rr][0]) print ('********',new_voc) #=============================================================== #=============================================================== #TEST print ('TEST') #Direct replace df_test['features']=df_test['features'].str.replace("garden/patio","garden") df_test['features']=df_test['features'].str.replace("patio","garden") df_test['features']=df_test['features'].str.replace("residents_garden","garden") df_test['features']=df_test['features'].str.replace("common_garden","garden") df_test['features']=df_test['features'].str.replace("wifi_access","wifi") df_test['features']=df_test['features'].str.replace("24/7","24") df_test['features']=df_test['features'].str.replace("24-hour","24") df_test['features']=df_test['features'].str.replace("24hr","24") df_test['features']=df_test['features'].str.replace("concierge","doorman") df_test['features']=df_test['features'].str.replace("ft_doorman","doorman") df_test['features']=df_test['features'].str.replace("24_doorman","doorman") df_test['features']=df_test['features'].str.replace("24_hr_doorman","doorman") df_test['features']=df_test['features'].str.replace("doorman_service","doorman") df_test['features']=df_test['features'].str.replace("full-time_doorman","doorman") df_test['features']=df_test['features'].str.replace("gym/fitness","fitness") df_test['features']=df_test['features'].str.replace("fitness_room","fitness") df_test['features']=df_test['features'].str.replace("washer","laundry") df_test['features']=df_test['features'].str.replace("laundry_in_bldg","laundry") df_test['features']=df_test['features'].str.replace("laundry_in_building","laundry") df_test['features']=df_test['features'].str.replace("laundry_in_building/dryer","laundry") df_test['features']=df_test['features'].str.replace("laundry_in_building_&_dryer","laundry") df_test['features']=df_test['features'].str.replace("laundry_room","laundry") df_test['features']=df_test['features'].str.replace("laundry_&_housekeeping","laundry") df_test['features']=df_test['features'].str.replace("laundry_in_unit","laundry") df_test['features']=df_test['features'].str.replace("laundry_in-unit","laundry") df_test['features']=df_test['features'].str.replace("laundry_on_every_floor","laundry") df_test['features']=df_test['features'].str.replace("laundry_on_floor","laundry") df_test['features']=df_test['features'].str.replace("in-unit_laundry/dryer","laundry") df_test['features']=df_test['features'].str.replace("on-site_laundry","laundry") df_test['features']=df_test['features'].str.replace("laundry/dryer","laundry") df_test['features']=df_test['features'].str.replace("high-speed_internet","high_speed_internet") df_test['features']=df_test['features'].str.replace("high_speed_internet_available","high_speed_internet") df_test['features']=df_test['features'].str.replace("parking_available","parking") df_test['features']=df_test['features'].str.replace("parking_space","parking") df_test['features']=df_test['features'].str.replace("on-site_garage","parking") df_test['features']=df_test['features'].str.replace("on-site_parking","parking") df_test['features']=df_test['features'].str.replace("on-site_parking_lot","parking") df_test['features']=df_test['features'].str.replace("full_service_garage","parking") df_test['features']=df_test['features'].str.replace("common_parking/garage","parking") df_test['features']=df_test['features'].str.replace("garage","parking") df_test['features']=df_test['features'].str.replace("assigned-parking-space","private_parking") df_test['features']=df_test['features'].str.replace("storage_available","storage") df_test['features']=df_test['features'].str.replace("storage_facilities_available","storage") df_test['features']=df_test['features'].str.replace("storage_space","storage") df_test['features']=df_test['features'].str.replace("storage_room","storage") df_test['features']=df_test['features'].str.replace("common_storage","storage") df_test['features']=df_test['features'].str.replace("central_a/c","central_air") df_test['features']=df_test['features'].str.replace("central_ac","central_air") df_test['features']=df_test['features'].str.replace("air_conditioning","central_air") df_test['features']=df_test['features'].str.replace("close_to_subway","subway") df_test['features']=df_test['features'].str.replace("roofdeck","roof_deck") df_test['features']=df_test['features'].str.replace("roof-deck","roof_deck") df_test['features']=df_test['features'].str.replace("rooftop_terrace","roof_deck") df_test['features']=df_test['features'].str.replace("rooftop_deck","roof_deck") df_test['features']=df_test['features'].str.replace("roof_access","roof_deck") df_test['features']=df_test['features'].str.replace("common_roof_deck","roof_deck") df_test['features']=df_test['features'].str.replace("swimming_pool","pool") df_test['features']=df_test['features'].str.replace("indoor_pool","pool") df_test['features']=df_test['features'].str.replace("deco_fireplace","fireplaces") df_test['features']=df_test['features'].str.replace("decorative_fireplace","fireplaces") #Word replace (_MMM_) aa=np.array(df_test['features']) #mel_count=df_test['features'].str.contains("**").sum() #print ('AFTER Replace-Join Section...*************',mel_count) aa=np.array(df_test['features']) df_test['features']=rep_join(aa,"gym_in_building","fitness") aa=np.array(df_test['features']) df_test['features']=rep_join(aa,"gym","fitness") aa=np.array(df_test['features']) df_test['features']=rep_join(aa,"health_club","fitness") aa=np.array(df_test['features']) df_test['features']=rep_join(aa,"fitness_center","fitness") aa=np.array(df_test['features']) df_test['features']=rep_join(aa,"pets_ok","pets_allowed") aa=np.array(df_test['features']) df_test['features']=rep_join(aa,"lounge_room","residents_lounge") aa=np.array(df_test['features']) df_test['features']=rep_join(aa,"wheelchair_ramp","wheelchair_access") #=============================================================== tfidf = CountVectorizer(stop_words='english', max_features=200,vocabulary=new_voc) #tfidf = CountVectorizer(stop_words='english', max_features=200) tr_sparse = tfidf.fit_transform(df_train["features"]) vocabulary_list=[] for key in tfidf.vocabulary_.items(): kk=key[0].split(', ') vocabulary_list.append(kk[0]) te_sparse = tfidf.transform(df_test["features"]) #============================================================================= #train_test section ntrain=df_train.shape[0] train_test = pd.concat((df_train, df_test), axis=0).reset_index(drop=True) train_test['bathrooms_cat'] = train_test['bathrooms'].apply(lambda x: str(x)) train_test['bathrooms_cat'], labels = pd.factorize(train_test['bathrooms_cat'].values, sort=True) df_train['bathrooms_cat']=np.array(train_test['bathrooms_cat'])[:ntrain] df_test['bathrooms_cat']=np.array(train_test['bathrooms_cat'])[ntrain:] train_test['bedrooms_cat'] = train_test['bedrooms'].apply(lambda x: str(x)) train_test['bedrooms_cat'], labels = pd.factorize(train_test['bedrooms_cat'].values, sort=True) df_train['bedrooms_cat']=np.array(train_test['bedrooms_cat'])[:ntrain] df_test['bedrooms_cat']=np.array(train_test['bedrooms_cat'])[ntrain:] buildings_count = train_test['building_id'].value_counts() train_test['top_5_building'] = train_test['building_id'].apply(lambda x: 1 if x in buildings_count.index.values[ buildings_count.values >= np.percentile(buildings_count.values, 95)] else 0) train_test['top_10_building'] = train_test['building_id'].apply(lambda x: 1 if x in buildings_count.index.values[ buildings_count.values >= np.percentile(buildings_count.values, 90)] else 0) train_test['top_25_building'] = train_test['building_id'].apply(lambda x: 1 if x in buildings_count.index.values[ buildings_count.values >= np.percentile(buildings_count.values, 75)] else 0) df_train['top_5_building']=np.array(train_test['top_5_building'])[:ntrain] df_test['top_5_building']=np.array(train_test['top_5_building'])[ntrain:] df_train['top_10_building']=np.array(train_test['top_10_building'])[:ntrain] df_test['top_10_building']=np.array(train_test['top_10_building'])[ntrain:] df_train['top_25_building']=np.array(train_test['top_25_building'])[:ntrain] df_test['top_25_building']=np.array(train_test['top_25_building'])[ntrain:] train_test=[] num_feats.append('manager_skill') num_feats.remove('bathrooms') num_feats.append('bathrooms_cat') num_feats.remove('bedrooms') num_feats.append('bedrooms_cat') num_feats.append('one_manager') num_feats.append('top_5_building') num_feats.append('top_10_building') num_feats.append('top_25_building') for ww in lista: num_feats.append(ww) print (list(df_train.columns) ) #=================================================================================== df_train = sparse.hstack([df_train[num_feats], tr_sparse]).tocsr() df_test = sparse.hstack([df_test[num_feats], te_sparse]).tocsr() print(df_train.shape, df_test.shape) print ('FINAL COLUMNS...',num_feats) #========================================================================== param = {} param['objective'] = 'multi:softprob' param['eta'] = 0.05 #0.016 param['max_depth'] = 6#6 param['silent'] = 1 param['num_class'] = 3 param['eval_metric'] = "mlogloss" param['min_child_weight'] = 1 param['subsample'] = 0.80#0.80 param['colsample_bytree'] = 0.7 #0.5 param['colsample_bylevel'] = 0.7 # #param['num_parallel_tree'] = 3 # param['seed'] = 1 param['lambda'] = 5 param['gamma'] = 1#1 num_rounds = 1200 plst = list(param.items()) param = {} param['objective'] = 'multi:softprob' param['eta'] = 0.05 #0.016 param['max_depth'] = 6#6 param['silent'] = 1 param['num_class'] = 3 param['eval_metric'] = "mlogloss" param['min_child_weight'] = 1 param['subsample'] = 0.80#0.80 param['colsample_bytree'] = 0.5 #0.5 param['colsample_bylevel'] = 0.7 # #param['num_parallel_tree'] = 3 # param['seed'] = 1 param['lambda'] = 5 param['gamma'] = 1#1 num_rounds2 = 900 plst2 = list(param.items()) param = {} param['objective'] = 'multi:softprob' param['eta'] = 0.005 #0.016 param['max_depth'] = 6#6 param['silent'] = 1 param['num_class'] = 3 param['eval_metric'] = "mlogloss" param['min_child_weight'] = 1 param['subsample'] = 0.85#0.80 param['colsample_bytree'] = 0.55 #0.5 param['colsample_bylevel'] = 0.7 # #param['num_parallel_tree'] = 3 # param['seed'] = 1 param['lambda'] = 1 param['gamma'] = 1#1 num_rounds = 1200 plst3 = list(param.items()) #========================================================================== #CV... cv=1 if cv : cv_scores = [] noa=0 kf = model_selection.KFold(n_splits=5, shuffle=True, random_state=2016) for dev_index, val_index in kf.split(range(df_train.shape[0])): noa+=1 print ('Training ..',noa) dev_X, val_X = df_train[dev_index,:], df_train[val_index,:] dev_y, val_y = yy[dev_index], yy[val_index] xgtrain = xgb.DMatrix(dev_X, label=dev_y) xgtest = xgb.DMatrix(val_X, label=val_y) watchlist = [ (xgtrain,'train'), (xgtest, 'test') ] model = xgb.train(plst, xgtrain, num_rounds, watchlist, early_stopping_rounds=15) pred_test_xgb = model.predict(xgtest) score1=log_loss(val_y,pred_test_xgb) model2 = xgb.train(plst2, xgtrain, num_rounds2, watchlist, early_stopping_rounds=15) pred_test_xgb2 = model2.predict(xgtest) score2=log_loss(val_y, pred_test_xgb2) #model3 = xgb.train(plst3, xgtrain, num_rounds, watchlist, early_stopping_rounds=15) #pred_test_xgb3 = model3.predict(xgtest) #score3=log_loss(val_y, pred_test_xgb3) print (noa,' *** XGB1 ',score1) print (noa,' *** XGB2 ',score2) #print (noa,' *** XGB3 ',score3) #Ansamble y_val_pred7=(pred_test_xgb +pred_test_xgb2 )/2 score=log_loss(val_y, y_val_pred7) print (noa,' 5/5/ XGB_ANS ',score) y_val_pred7=(0.3*pred_test_xgb +0.7*pred_test_xgb2 ) score=log_loss(val_y, y_val_pred7) print (noa,' 3/7 XGB_ANS ',score) y_val_pred7=(0.7*pred_test_xgb +0.3*pred_test_xgb2 ) score=log_loss(val_y, y_val_pred7) print (noa,' 7/3 XGB_ANS ',score) y_val_pred7=(0.25*pred_test_xgb +0.75*pred_test_xgb2 ) score=log_loss(val_y, y_val_pred7) print (noa,' 25/75 XGB_ANS ',score) y_val_pred7=(0.75*pred_test_xgb +0.25*pred_test_xgb2 ) score=log_loss(val_y, y_val_pred7) print (noa,' 75/25 XGB_ANS ',score) break #================================================ #================================================ #ACTUAL TRAINING print ('Actual training...') num_rounds=1100 num_rounds2=1000 xgtrain = xgb.DMatrix(df_train, label=yy) xgtest = xgb.DMatrix(df_test) model = xgb.train(plst, xgtrain, num_rounds) pred_test_xgb = model.predict(xgtest) print ('XGB1 train OK') model2 = xgb.train(plst2, xgtrain, num_rounds2) pred_test_xgb2 = model2.predict(xgtest) print ('XGB2 train OK') #model3 = xgb.train(plst3, xgtrain, num_rounds) #pred_test_xgb3 = model3.predict(xgtest) #print ('XGB3 train OK') y=(0.75*pred_test_xgb +0.25*pred_test_xgb2 ) #================================================ #Output setup sub = pd.DataFrame() sub["listing_id"] = llista ff0=np.zeros(len(y)) ff1=np.zeros(len(y)) ff2=np.zeros(len(y)) for rr in range(len(y)): ff0[rr] = y[rr][0] ff1[rr] = y[rr][1] ff2[rr] = y[rr][2] sub['high']=ff0 sub['medium']=ff1 sub['low']=ff2 sub.to_csv("subxansa3.csv", index=False)